Image Fusion a survey Image Fusion a survey

Vol.:(0123456789)
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Archives of Computational Methods in Engineering (2021) 28:4425–4447
https://doi.org/10.1007/s11831-021-09540-7
ORIGINAL PAPER
Image Fusion Techniques: A Survey
Harpreet Kaur1
· Deepika Koundal2
· Virender Kadyan3
Received: 17 May 2020 / Accepted: 10 January 2021 / Published online: 24 January 2021
© CIMNE, Barcelona, Spain 2021
Abstract
The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acqui-
sition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various
mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary
multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping
the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot
vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image
fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics
and their applications in different domains have been discussed. Finally, this review has concluded various future directions
for different applications of image fusion.
1 Introduction
Image fusion (IF) is an emerging field for generating an
Infrmative image with the integration of images obtained by
different sensors for decision making [1]. The analytical and
visual image quality can be improved by integrating differ-
ent images. Effective image fusion is capable of preserving
vital Information by extracting all important Information
from the images without producing any inconsistencies in
the output image. After fusion, the fused image is more suit-
able for the machine and human perception. The first step
of fusion is Image Registration (IR) in which source image
is mapped with respect to the reference image. This type of
mapping is performed to match the equivalent image on the
basis of confident features for further analysis. IF and IR are
perceived as vital assistants to produce valuable Informa-
tion in several domains [2]. According to the literature, the
number of scientific papers has been increased dramatically
since 2011 and reached to the peak 21,672 in 2019 which
can be illustrated in Fig. 1. This fast-rising trend can be rec-
ognized due to the increased demand for high-performance
image fusion techniques with low cost. Recently, various
techniques like multi-scale decomposition and sparse rep-
resentation have been introduced that bring several ways for
improving the image fusion performance. There is a need
for efficient fusion method due to variations between cor-
responding images in various applications. For instance,
numerous satellites are increasing nowadays to acquire aerial
images with diverse spectral, spatial and temporal resolu-
tions in the domain of remote sensing. The IF is basically a
collection of image Information achieved by several imag-
ing parameters such as aperture settings or dynamic range,
spectral response or position of the camera or the use of
polarization filters. The Information of interest is extracted
from different images with the help of appropriate image
fusion methods which can further be used for traffic control,
reconnaissance, driver assistance or quality assessment.
Various techniques of image fusion can be classified as
pixel level, decision level and feature level. Pixel level tech-
niques for image fusion directly integrate the Information
from input images for further computer processing tasks
* Deepika Koundal
koundal@gmail.com
Harpreet Kaur
harpreet.kaur.045@gmail.com
Virender Kadyan
vkadyan@ddn.upes.ac.in
1
Department of Computer Science, Chitkara University
Institute of Engineering and Technology, Chitkara
University, Punjab, India
2
Department of Virtualization, School of Computer
Science, University of Petroleum and Energy Studies,
Bidholi, Dehradun, India
3
Department of Informatics, School of Computer
Science, University of Petroleum and Energy Studies,
Bidholi, Dehradun, India

4426 H. Kaur et al.
1 3
[3]. Feature level techniques for image fusion entails the
extractions of relevant features that is pixel intensities, tex-
tures or edges that are compounded to create supplementary
merged features [4, 5]. In decision level fusion techniques
for images, the input images are processed one at a time
for the extraction of Information [4]. There are a variety of
IF classifications based on the datasets such as multi-focus,
multi-spectral, multi-scale, multi-temporal, and multi-sen-
sor. In multi-focus techniques for image fusion, Informa-
tion from several images of a similar scene is fused to get
one composite image [6]. In addition, the multi-source and
multi-sensor IF methods recommend superior features for
representing Information which is not visible to a human
visible system and are utilized for medical diagnosis applica-
tions. The Information generated from merged images can
be employed for localization of abnormalities accurately [7].
The temporal modeling will give details of all clinical vari-
ables and reduce the risk of Information failure [8]. The fast-
rising trend can be a major factor of image fusion techniques
having low cost and high performance.
Currently, several techniques like sparse representation
(SR) and multi-scale decomposition have been anticipated that
help in enhancing the image fusion performance [3]. SR is a
theory of image representation, which is employed to an image
processing tasks such as interpolation, denoising, and recog-
nition [1]. The multi-spectral (MS) image is used for remote
sensing that merges their features to get an understandable
image using the corresponding Information and spatiotemporal
correlation [9]. IF has grown to be an influential solution by
merging the images captured through diverse sensors. Images
of diverse types such as infrared, visible, MRI and CT are
superior input images for multimodal fusion [1]. Currently,
Deep learning is a very active topic in image fusion. It has
gained great success in this area for solving different type of
problems such as image processing and computer vision. It
is widely used for image fusion [10]. Due to recent techno-
logical advancements, various imaging fusion techniques have
been utilized in many applications including video surveil-
lance, security, remote sensing, machine vision, and medical
imaging.
Still there are number of challenges associated with image
fusion which have to be explored. Moreover, an appropriate,
accurate and reliable fusion technique is required for the vari-
ous types of images for different domains that should be easily
interpretable to obtain better results. Besides, image fusion
techniques must be robust against uncontrollable acquisition
conditions or inexpensive computation time in real-time sys-
tems as mis-registration is a major error found while fusing
images. This paper presents an outline of various IF techniques
with their applications in diverse domains. Also, various chal-
lenges, shortcomings, and benefits of image fusion techniques
have been discussed.
Rest of the paper is organized in different sections. Sec-
tion 2 discusses the image fusion process. Section 3 gives the
various image fusion techniques. Section 4 presents the taxo-
nomical view of the image fusion methods. Section 5 explains
various image fusion applications. In Sect. 6, the evaluation
metrics of fusion are discussed. Section 7 delivered the percep-
tive deliberations and prospects for future work. Finally, the
papers with an outline of major ideas are concluded.
Fig. 1 According to the lit-
erature, the number of articles
related to image fusion

4427
1 3
2
Image Fusion Process
As discussed earlier, the goal of IF is to produce a merged
image with the integration of Information from more than
one image. Figure 2 demonstrates the major steps involved
in IF process. In wide-ranging, the registration is meas-
ured as an optimization issue which is used to exploit the
similarity as well as to reduce the cost. The Image registra-
tion procedure is used to align the subsequent features of
various images with respect to a reference image. In this
procedure, multiple source images are used for registration
in which the original image is recognized as a reference
image and the original images are aligned through refer-
ence image. In feature extraction, the significant features
of registered images are extracted to produce several fea-
ture maps.
By employing a decision operator whose main objective
is to label the registered images with respect to pixel or
feature maps, a set of decision maps are produced. Seman-
tic equivalence obtained the decision or feature maps that
might not pass on to a similar object. It is employed to
connect these maps to a common object to perform fusion.
This process is redundant for the source obtained from
a similar kind of sensors. Then, radiometric calibration
is employed on spatially aligned images. Afterward, the
transformation of feature maps is performed on an ordi-
nary scale to get end result in a similar representation for-
mat. Finally, IF merge the consequential images into a one
resultant image containing an enhanced explanation of the
image. The main goal of fusion is getting more Infrmative
fused image [2].
3 Image Fusion Techniques
IF techniques can be classify as spatial and frequency
domain. The spatial technique deals with pixel values of
the input images in which the pixels values are manipu-
lated to attain a suitable outcome. The entire synthesis
operations are evaluated using Fourier Transform (FT) of
the image and then IFT is evaluated to obtain a resulting
image. Other IF techniques are PCA, IHS and high pass
filtering and brovey method [12].
Discrete transform fusion techniques are extensively
used in image fusion as compared to pyramid based fusion
technique. Different types of IF techniques are shown in
Fig. 3 [13].
3.1 Spatial Based Techniques
The Spatial based technique is a simple image fusion
method consist of Max–Min, Minimum, Maximum, Sim-
ple Average and Simple Block Replace techniques [14]
[15]. Table 1shows the diverse spatial domain based meth-
ods with their pros and cons.
3.1.1 Simple Average
It is a fusion technique used to combined images by aver-
aging the pixels. This technique focused on all regions
of the image and if the images are taken from the same
type of sensor then it works well [16]. If the images have
high brightness and high contrast then it will produce good
results.
3.1.2 Minimum Technique
It selects the lowest intensity value of the pixels from
images andproduceda fused image [14]. It is used for
darker images [17].
3.1.3 Maximum Technique
It selects the pixels values of high intensity from images
to yield fused image [12].
3.1.4 Max–Min Technique
It selects the averaging values of the pixels smallest and
largest from the entire source images and produced the
resultant merged image.
Fig. 2 The main steps of IF procedure

4428 H. Kaur et al.
1 3
3.1.5 Simple Block Replace Technique
It adds all images of pixel values and takes the block aver-
age for it. It is based on pixel neighboring block images.
3.1.6 Weighted Averaging Technique
It assigned the weights to every pixel in the source images.
The resultant image is produced by the weighted sum
Fig. 3 Image Fusion Techniques
Table 1 shows the diverse spatial domain based methods with their pros and cons as per the literature review
Fusion Techniques Advantages Disadvantages
Averaging [14, 21]
Minimum pixel value [14, 21]
Simple block replacement [15]
Maximum pixel value [14, 21]
Max- min [15]
Simple, easy to recognize and implement Decreases the image quality and reduces noise into
final fused resultant image
Produced blurred images. Not appropriate for real time
applications
Weighted averaging [18] Improves the detection reliability Enhances the SNR
Principal component based
analysis [22, 23]
Simple and more efficient, high spatial quality, lesser
computational time
Resulted in color distortion and spectral degradation
Hue intensity saturation[21] Efficient and simple. high sharpening ability and Fast
processing
Color distortion
Brovey [14] Extremely straightforward and more efficient method.
Faster processing time. Gives Red–Green–Blue
images with superior degree of contrast
Color distortion
Guided filtering[24] It performs well in image smoothing or enhancement,
flash or no-flash imaging, matting or feathering and
joint upsampling
On the sparse inputs it is not directly applicable. It has
a common drawback; it may have halos near some
edges like other explicit filters

4429
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of every pixel value in source images [18]. This method
improves the detection reliability of the output image.
3.1.7 Hue Intensity Saturation (HIS)
It is a basic fusion color technique that converted the
Red–Green–Blue image into HIS components and then
intensity levels are divided with panchromatic (PAN)
image. Spatial contains intensity Information and spectral
contains both hue and saturation Information of the image.
It performs in the bands and has three multispectral bands
Red–Green–Blue (RGB) of low resolution. In the end, the
inverse transformation is performed to convert the HIS space
to the original RGB space for yielding fused image [12]. It
is a very straightforward technique to combine the images
features and provides a high spatial quality image. In remote
sensing images it gives the best result and major drawback
is that it involved only three bands [19].
3.1.8 Brovey Transform Method
Gillespie et al. suggested Brovey Transform in 1987. It is
a straightforward technique for merging data from more
than one sensor. It overcomes the three band problems. It
standardized the three multispectral bands used for RGB to
append the intensity and brightness into the image [13]. It
includes an RGB color transform technique that is known as
color normalization transform to avoid disadvantages of the
multiplicative technique [12]. It is helpful for visual Inter-
pretation but generates spectral distortion [19].
3.1.9 Principal Component Analysis (PCA)
It is a statistical method on the basis of orthogonal trans-
formation for converting a set of observations of a possibly
correlated variable into principal components that are set of
linearly uncorrelated variables. The main drawback of PCA
is spectral degradation and color distortion [9].
3.1.10 Guided filtering
It works as a boundary smoothing and preserving opera-
tor similar to the admired bilateral filter. It has enhanced
performance near the boundaries. It has a hypothetical link
with Laplacian matrix. It is a fast and non-estimated linear
time algorithm, whose density is not dependent on the mask
size. This filter is more efficient and effective in graphics and
computer vision applications with joint upsampling, haze
removal, detail smoothing and noise reduction [20]. IF is
also used in medical domain to identity the various diseases.
In which article, author perform experiment on brain images
and prove that Guided filter provides better results as com-
pared to Principal component analysis and multi-resolution
singular value decomposition technique [161].
3.2 Frequency Domain
These techniques decomposed the multiscale coefficients
from the input images [25]. Spatial distortion can be handled
by the frequency method. Table 2 lists the various frequency
domain based methods with their pros and cons.
Table 2 shows the diverse frequency domain based methods with their pros and cons as discussed by several authors
Laplacian/Gausian pyramid [27, 28]
Low pass pyramid ratio [29]
Morphological pyramid [28]
Gradient pyramid [30]
Filter subtract decimate [30]
Provides better image quality of a representa-
tion for multi focus images
Provide almost same result. Number of break-
down levels affects the IF results
Discrete cosine transform (DCT) [38] Decomposed images into series of waveform
and used for real time applications
Low quality fused image
Discrete wavelet technique with Haar fusion
[39]
Produced better quality of fused image with
good SNR. Reduced the spectral distortion
Merged image has fewer spatial resolutions
Kekre’s wavelet transform fusion [43, 44] Used for any size of images and its final fused
result is more Infrmative
Computationally complex
Kekre’s hybrid wavelet based transform fusion
[45, 46]
It gives better results It cannot be used images integer power of two
Stationary wavelet transform (SWT) [35–37] Give better result at level 2 of decomposition Time consuming process
Stationary wavelet transform (SWT) and Cur-
velet Transform
Suitable for real time applications Very time consuming process

4430 H. Kaur et al.
1 3
3.2.1 Laplacian Pyramid Fusion Technique
It uses the interpolation sequence and Gaussian pyramid for
multi-resolution analysis for image fusion. Saleem et al. have
reported an improved IF technique using a contrast pyramid
transform on multi-source images [151]. But it is suffered by
the drawback of extraction ability which can be overcome by
multi-scale decomposition. Further, Li et al. improved the
gradient pyramid multi-source IF method which attains high
band coefficient with the help of gradient direction opera-
tor [9].
3.2.2 Discrete Transform Fusion Method [14]
Discrete transform based fusion take composite
images. Firstly, if the images are colored then RGB
(Red–Green–Blue) components of the multiple images are
separated subsequently, discrete transformation on images is
applied and then the average of multiple images is computed
an inverse transformation is applied at the end to obtain a
fused image. DWT (Discrete wavelets transform) is a better
IF method as compared to other fusion methods like Lapla-
cian pyramid method, Curvelet transforms method etc [26].
3.2.3 Discrete Cosine Transform (DCT)
In image fusion, DCT has various types like DCTma (DCT
magnitude), DCTcm (DCT contrast measure), DCTch (DCT
contrast highest), DCTe (DCT energy) and DCTav (DCT
average) [29]. This technique does not give a better result
with the size of the block less than 8×8. In the DCT domain,
DCTav is straightforward and basic method of image fusion.
DCTe and DCTma methods performed well in image fusion.
This technique is straightforward and used in factual time
applications.
3.2.4 Discrete Wavelet Transform (DWT) Method
DWT method decomposes the two or more images into vari-
ous high and low-frequency bands [31]. This method mini-
mized the spectral distortion in the resultant fused images by
producing the good signal to noise ratio with fewer spatial
resolution as compared to the pixel-based method. Wave-
let fusion performed superior to the spatial domain fusion
method with respect to minimizing the color distortions [15].
3.2.5 Kekre’s Wavelet Transform (KWT) Method
Kekre’s Wavelet Transform method is obtained from
Kekre’s transforms [32]. It can generate KWT matrices of
((2 N)*(2 N)), ((3 N)*(3 N)),…., ((N2)*(N2)) from Kekre’s
transform method matrix [33]. It can be used for more
than one images and the fused image is far good than other
methods.
3.2.6 Kekre’s Hybrid Wavelet Transform (KHWT) Method
KHWT method has been derived from hybrid wavelet
transforms. Many authors suggested that kekre-hadamard
wavelet method gives more brightness. Hybrid kekre-DCT
wavelet method gives good results. In this method, the best
two matrices are combined into a hybrid wavelet transforms
method. It cannot be used images integer power of two [45].
3.2.7 Stationary Wavelet Transform (SWT) Method
DWT method has a disadvantage of translation invariance
and Stationary Wavelet Transform overcome this problem
[34]. This technique provides a better output at decompo-
sition level 2 and time inefficient process [35] [36] [37].
SWT derived from DWT method. It is a new type of wavelet
transform method with translation invariance. It provides
enhanced analysis of image facts. The next second invention
curvelet transform method is additionally suitable for 2-D
image edges.
3.2.8 Curvelet Transform Method
SWT has a better characteristic in time–frequency. It can
achieve well result for devising in smooth. The second gen-
eration Curvelet is a new multi-scale transform; it breaks the
disadvantages of wavelet method in representing directions
of boundaries in the image [11, 40–42].
3.3 Deep Learning
Another technique which is most widely used for image
fusion is Deep Learning in various domains. Several deep
learning based image fusion methods have been presented
for multi-focus image fusion, multi-exposure image fusion,
multi-modal image fusion, multi-spectral (MS) image
fusion, and hyper-spectral (HS) image fusion, show-
ing various advantages. Various recent advances related
to deep learning based image are discussed in [10]. In
another way, deep learning and case-based reasoning tech-
niques are used with image fusion to enhance the out-
come of segmentation. In this article, author used artificial
intelligence to improve the results of segmentation and
its implementation done on kidney and tumour images.
This process complete in three layers: Fusion layer, Seg-
mentation layer, Data layer [159]. The multi-view deep
learning model is also used in Covid-19 for validation and
testing sets of chest CT images. It is more helpful to iden-
tify the diagnosis problem. Data is collected from various

4431
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hospitals of china [160]. The reasons behind the popularity
of deep learning based methods for image fusion are pre-
sented as that deep learning model are able to extract the
most elective features automatically from data without any
human intervention. These models are also able to char-
acterize various complex relationships between targeting
data and input. Deep learning models are gaining popular-
ity in providing potential image representation approaches
which could be useful to the study of image fusion. Com-
monly used deep learning models in image fusion are Con-
volutional Neural Network (CNN), Convolutional Sparse
Representation (CSR) and Stacked Autoencoder (SAE).
Table 3.listed the various advantages and disadvantages
of deep learning based image fusion methods.
4
Image Fusion categorization
It is the process which integrates the source image and
reference image into one image. Diverse techniques were
anticipated by different authors to achieve the required
fusion objective. A Single sensor, multi-sensor, multi-
modal, multi-view, multi-focus, and multi-temporal illus-
trates major classes of such methods which are discussed
below.
4.1 Single Sensor
A number of images are merged to produce a fused image
with the best possible Information. For instance, human
operators are not able to perceive desired objects in light-
ing variant and noisy environment which can be high-
lighted in the end fused image. The inadequacy of this
type of system is due to imaging sensors that are used in
many sensing areas. The resolution of images is limited
by the sensors and the conditions in which the system is
operated in its dynamic range. Such as visible band sensor
(digital camera) is appropriate for illuminated day-light
scenes but is not appropriate for the badly illuminated
nighttime environment or under fog or rain that is unfa-
vorable conditions [47].
4.2 Multi Sensors
Multi-sensor IF overpowers the confines of a one sensor IF
by integration of images from a number of sensors to form
a compound image. An infrared (IR) camera accompanies
the digital camera to obtain the final image from individual
images. The infrared camera is suitable in inadequate illu-
minated environments and the digital camera is suitable for
day-light views. It is used in machine vision, a military area
such as in medical imaging, robotics and object detection.
It is mostly used to resolve the merged Information of the
numerous images [47]. According to the literature review,
Table 4 shows the various Multi-sensors techniques dis-
cussed by several authors.
4.3 Multi‑view Fusion
Multi-view images have diverse views at the similar time.
It is also known as Mono-modal fusion [47]. The existing
methods didn’t achieve acceptable performances in all cases,
especially when one of the estimations is not high-quality; in
this case, they are unable to discard it [49, 86, 152]. Accord-
ing to the literature review, Table 5 shows the various Multi-
view techniques discussed by several authors.
4.4 Multi‑modal Fusion
It is the process of integrating multi-modal images from one
or more imaging modalities to enhance the quality of an
image. The various models are multispectral, panchromatic,
infrared, remote sensing and visible images. Table 6 evident
the various Multi-modal techniques discussed by several
authors according to the literature review.
4.5 Multi‑focus Fusion
It is an efficient method for integrating the Information
from several images with a similar sight into a wide-rang-
ing image. The compound image is more Infrmative than
Table 3 shows deep learning based image fusion methods
CNN [153–156] Able to extract features and representation can learn most elective features from
training data without any human intervention
High computational cost
CSR [157] Compute sparse representation of an entire image shift-invariant representation
approach elective in details preservation less sensitive to mis-registration
Need a lot of training data
SAE [158] Two phase based training mechanism have a high potential when the scale of labeled
data for supervised learning is limited
If you don’t have a good GPU
they are quite slow to train (for
complex tasks)

4432 H. Kaur et al.
1 3
Table
4 Multi
Sensor
image
fusion
techniques
reported
in
literature
Here
p
represents
the
literature
review
on
different
fields
in
various
categorizations
by
different
authors
NN,
Neural
network,
DWT,
Discrete
wavelet
transform,
DCT,
Discrete
cosine
transform,
PNN,
Pulse-coupled
neural
network,
WT,
Wavelet
transform,
SWT,
Stationary
wavelet
transform,
ANN,
Artificial
neural
network,,
SI-DWT,
Shift
invariant
discrete
wavelet
transform,
PCNN,
Pulse-coupled
neural
network,
PCA,
Principal
component
analysis,
NSCT,
Non-subsampled
contourlet
transform,
CT,
Contourlet
transform,
HIS,
Hue
intensity
saturation,
DT-CWT
,
Dual-tree
complex
wavelet
transform,
HVS,
Hue
value
saturation,
RVS,
Regression
variable
substitution.
HPF,
High
pass
filter,
SVR,
Synthetic
variable
ratio
Authors
Neural
networks
classifications
Wavelet
based
classifications
Contourlet
based
clas-
sifications
Other
classifications
NN
ANN
PNN
PCNN
WT
DWT
SWT
DCT
DT-CWT
SI-DWT
CT
NSCT
PCA
IHS
Entropy
Fuzzy
HVS
RVS
HPF
SVR
Paramanandham
[48]
p
p
p
p
p
p
p
p
Ehlers
[49]
p
p
p
Yoonsuk
Choi
[50]
p
p
p
p
p
Ross
[51]
p
p
Li
[52]
p
Egfin
Nirmala
[53]
p
p
p
Pohl
[54]
p
p
p
p
p
p
Uttam
Kumar
[55]
p
p
p
Meti
[56]
p
p
p
p
Makode
[57]
p
p
p
p
p
Chang
[58]
p
p
p
p
Jiang
[15]
p
p
p
p
p
Hall
[59]
p
p
Satish
Kumar
[60]
p
Pawar
[61]
p
p
p
p
p
p
Lemeshewsky
[62]
p
p
p
p
Dengi
[63]
p
p
Li
[64]
p
Zheng
[65]
p
Li
[66]
p
p
p
p

4433
1 3
Table
5 Multi-View
image
fusion
techniques
reported
in
literature
Here
p
represents
the
literature
review
on
different
fields
in
various
categorizations
by
different
authors
CNN,
Convolution
neural
network,
KNN,
K
nearest
neighbor,
PSNR,
Peak
signal
to
noise
ratio,
LSFM,
Light
sheet
fluorenscence
microscopy,
AVG,
Averaging,
SNR,
Signal
to
noise
ratio,
KPCA,
Kernal
principal
component
analysis,
TP,
Temporal
projection,
TMIP,
Temporal
motion
interpolation
projection,
SVM,
Support
vector
machines,
DVC,
Distributed
video
coding,
ICA,
Independ-
ent
component
analysis
Authors
Neural
networks
clas-
sification
Wavelet
classifications
Contourlet
classifications
Other
classifications
CNN
KNN
WT
DWT
DCT
CT
NSCT
PSNR
DVC
LSFM
AVG
SNR
PCA
KPCA
SVM
FUZZY
ICA
TP
TMIP
Petrazzuoli
[67]
p
p
Cheung
[68]
p
Gelman
[69]
p
p
Maugey
[70]
p
p
p
Artigas
[71]
p
p
Jose
L
[72]
p
p
p
C.
Guillemat
[73]
p
p
Guo
[74]
p
p
p
Wang
[75]
p
p
Rajpoot
[76]
p
p
p
Forre
[77]
p
p
p
Zhang
[78]
p
p
Yongpeng
Li
[79]
p
Frederic
Dufaux
[80]
p
p
p
p
Das
[81]
p
p
Swoger
[82]
p
Seng
[83]
p
Rahul
Kavi
[84]
p
p
Kiska
[85]
p
p
p
p
p
Liu
[81]
p

4434 H. Kaur et al.
1 3
Table
6 Multi-Modal
image
fusion
techniques
reported
in
literature
Here
p
represents
the
literature
review
on
different
fields
in
various
categorizations
by
different
authors
CWT
,
Complex
wavelet
transform,
DDWD,
Dual-tree
wavelet
transform,
PSO,
Particle
swarm
optimization,
MWT,
Wavelet
transform
Authors
Neural
networks
clas-
sifications
Wavelet
based
classifications
Contourlet
based
classifi-
cations
Other
classifications
NN
PCNN
ANN
WT
DWT
SWT
CWT
MWT
DDWD
DTCWT
CT
NSCT
PSO
PCA
Avg
Entropy
Fuzzy
logic
Wei
li
et
al.
[87]
p
p
Yong
et
al.
[88]
p
Max
et
al.
[89]
p
p
Kor
et
al.
[90]
p
Deron
et
al.
[91]
p
Zhao
et
al.
[92]
p
p
Richa
et
al.
[93]
p
Guihong
Qu
et
al.
[94]
p
Wang
et
al.
[95]
p
Sharmila
et
al.
[96]
p
p
p
p
Rajiv
et
al.
[97]
p
p
p
p
p
p
p
Bhavana
et
al.
[98]
p
p
Anitha
et
al.
[99]
p
p
p
Anjali
et
al.
[100]
p
p
Periyavattam
et
al.
[101]
p
Pradeep
et
al.
[102]
p
p
Guruprasad
et
al.
[103]
p
p
Kiran
et
al.
[104]
p
Anna
et
al.
[95]
p
Gaurav
et
al.
[105]
p
p
p
p
Amir
et
al.
[106]
p
Devanna
et
al.[107]
p
p
p
p
p
p
Senthil
et
al.[105]
p
p

4435
1 3
Table
7 Multi-Focus
fusion
techniques
reported
in
literature
Here
p
represents
the
literature
review
on
different
fields
in
various
categorizations
by
different
authors
SPCNN,
Standard
PCNN,
LSW,
Lifting
stationary
wavelet,
MWT,
Wavelet
transform,
DDWD,
Dual-tree
wavelet
transform,
EOG,
Energy
of
image
gradient,
HMM,
Hidden
markov
modeling,
PSO,
Particle
swarm
optimization
Authors
Neural
networks
classifications
Wavelet
based
classifications
Contourlet
based
clas-
sifications
Other
classifications
ANN
PNN
PCNN
SPCNN
DWT
SWT
CWT
LSW
DCT
DT-CWT
CT
NSCT
NSST
PCA
HIS
Entropy
Fuzzy
EOG
HMM
Avg
Maruthi
[4]
p
p
p
p
p
p
p
p
p
p
p
p
p
p
Wang
[95]
Bhatnagar
[105]
Anish
[108]
p
p
p
p
p
p
p
p
p
Li
[109]
p
p
p
p
Wang
[110]
p
p
p
Li
[111]
p
p
p
Huang
[112]
p
p
p
Garg
[113]
p
p
p
Kaur
[114]
p
p
p
p
p
p
p
p
Kaur
[115]
p
p
Liu
[116]
p
Malhotra
[117]
p
p
Sulaiman
[118]
p
p
p
p
Li
[119]
p
p
p
Haghigha
[120]
p
p
p
Tian
[121]
p
Yang
[122]
p
p
p
p
Malik
[123]
p
p
Chai
[124]
p
p
Qu
[125]
p
p
p
Maruthi
[126]
p
p

4436 H. Kaur et al.
1 3
Table
8 Multi-Temporal
fusion
techniques
reported
in
literature
Here
p
represents
the
literature
review
on
different
fields
in
various
categorizations
by
different
authors
CSS,
Content
selection
strategy,
CVA,
Change
vector
analysis,
SVM,
Support
vector
machine,
ICC,
Iterative
compound
classification,
PCC,
Post
classification
comparison,
CD,
Change
detec-
tion,
LDP,
Local
derivative
pattern,
OCE,
Overall
cross
entropy,
HPF,
High
pass
filter
Authors
Neural
networks
clas-
sifications
Wavelet
based
classifications
Contourlet
based
classi-
fications
Other
classifications
PNN
DWT
DT-DWT
SI-DWT
CT
NSCT
PCA
HIS
CSS
CVA
SVM
ICC
PCC
CD
LDP
PSNR
OCE
FUZZY
ENTROPY
HPF
Anitha
[127]
p
p
p
Pawar
[128]
p
Parthiban
[129]
p
p
p
Momeni
[130]
p
p
p
p
Han
Pan
[131]
p
p
p
p
p
p
Silivana
[132]
p
Jain
[133]
p
p
Ferretti
[134]
p
Peijun
Du
[135]
p
p
p
p
Wang
[136]
p
Mittal
[137]
p
p
p
p
Wisetphanichkij
[138]
p
p
Visalakshi
[139]
p
p
p
p
p
p
Bovalo
[140]
p
Liu
[141]
p
p
p
Celik
[142]
p
p
p
p
Xiaojun
Yang
[143]
p
p
Bruzzone
[144]
p
p
p
Demir
[145]
p
p
p
Zhong
[146]
p
p

4437
1 3
input images [6]. It gives better visual quality of an image.
According to the literature review, Table 7 shows the various
Multi-focus techniques discussed by several authors.
4.6 Multi‑temporal Fusion
Multi-temporal fusion captures the same scene at differ-
ent times. Long and short-term observations are required
because of the estimation of the occurrence of changes on
the ground. Because of the revisit observation satellites,
remote sensing images are obtained at diverse times for a
given area. Multi-temporal images are vital for detecting
land surface variations in broad geographical areas. Accord-
ing to the literature review; Table 8 shows the various Multi-
temporal techniques discussed by several authors.
5
Main Applications in Diverse Domains
In current years, IF has been widely used in many different
applications such as medical diagnosis, surveillance, pho-
tography and remote sensing. Here, various challenging and
issues are discussed related to different fields [3].
5.1 Remote Sensing Applications
In accumulation to the modalities discussed above, it has
numerous IF techniques such as Synthetic Aperture Radar,
ranging and light detection and moderate resolution imaging
spectroradiometer that have been useful in IF applications.
Byun et al. have given the area based IF scheme for combin-
ing panchromatic, multispectral and synthetic aperture radar
images [1]. Temporal data fusion and high spatial approach
is used to produce synthetic Landsat imagery by combining
Landsat and moderate resolution imaging spectroradiometer
data [1]. Moreover, the synthesis of air-bone hyper-spec-
tral and Light Detection and Ranging (LiDAR) data is
researched recently by a combination of spectral Informa-
tion. Various datasets have been provided by Earth imaging
satellites like Quickbird, Worldview-2, and IKONOS for the
applications of pansharpening. Co-registered hyper-spectral
and multispectral images are more complex to obtain as
compared to multispectral and panchromatic images. Moreo-
ver, air-bone hyper-spectral data and LiDAR are accessible.
For occurrence, the IEEE Geoscience and Remote Sensing
Society Data Fusion 2013 and 2015 Contests have distrib-
uted numerous hyper-spectral, color and light detection and
ranging data for research purposes. In this field application,
numbers of satellites are mounted to acquire remote sens-
ing images with diverse spatial, temporal and spectral reso-
lutions. Moreover, in this field, classification and change
detection has been providing by Google Maps or Earth prod-
ucts that are effectively applied to construct the imagery
seen. This is a supplementary difficult problem as compared
with pansharpening, the multichannel multispectral image
contains both spatial Information and spectral Information.
Therefore, pansharpening is unsuitable or incompetent for
the IF of hyperspectral and multispectral images. The fore-
most challenge in this domain is accomplished as below:
(1) Spatial and spectral distortions The image datasets
frequently reveal variations in spatial and spectral
structures which causes more distortions with spatial
or spectral artifacts during image fusion.
(2) Mis-registration The next most important challenge in
this domain is how to decrease the misregistration rate.
The remote sensing input images are regularly obtained
from diverse times, acquisitions or spectral bands. Even
the panchromatic and multispectral datasets provided
by a similar platform, the one or more sensors may not
Fig. 4 Examples of IF in remote sensing domain. a PAN b MS c Fused image

4438 H. Kaur et al.
1 3
give accurate results in the same direction; their gain-
ing moments may be different. Therefore, in order to
resolve this, prior to IF, the images are required to be
registered. Conversely, registration is the challenging
process because of the variations between input images
as they are provided with diverse acquisitions. Figure 4
shows the fusion of Panchromatic and Multi-spectral
images that is achieved by the Principal Component
Analysis (PCA) transformation correspondingly.
5.2 Medical Domain Applications
Harvard Medical School has provided a brain image data-
set of registered Computerized Tomography and Magnetic
Resonance Imaging. Figure 6 shows an example of IF in
medical diagnosis by fusing CT and MRI. The CT is used
for capturing the bone structures with high spatial resolu-
tions and MRI is used to capture the soft tissue structures
like the heart, eyes, and brain. The CT and MRI can be
used collectively with IF techniques to enhance accuracy
and sensible medical applicability. The main challenging
of this field is also accomplished as below.
(1) Lack of medical crisis oriented IF methods The main
motive of IF is to assist the improved clinical results.
The clinical crisis is still a big challenge and nontrivial
tasks in the medical field.
(2) Objective image fusion performance estimate The main
difficulty in this domain is how to evaluate the IF per-
formance. There are diverse clinical issues of IF, which
preferred the IF effect may be fairly dissimilar.
(3) Mis-registration The inaccurate registration of objects
suffered from poor performance in the medical domain.
Figure 5 illustrates the fusion of MRI and CT images.
In this, the fusion of images is achieved by the guided
filtering based technique with image statistics.
Fig. 5 Examples of IF in medical diagnosis domain. a MRI b CT c Fused image
Fig. 6 Examples of IF in surveillance domain. a Visible image b Infrared image c Fused image

4439
1 3
5.3 Surveillance Domain Applications
Figure 6 shows examples of IF in the surveillance domain
that is infrared and visible images fusion. Its high tempera-
ture makes it able to “see in the night” even without enlight-
enment as it is sensitive to objects. Infrared images give bad
spatial resolution and it can be overcome by fusion technique
by the visible and infrared image. Moreover, the fusion of
visible and infrared images has been introduced for another
surveillance domain problem in face recognition, image
dehazing, and military reconnaissance. The main challenges
of in this domain are as:
(1) Computing efficiency In this domain, effective IF algo-
rithms should merge the Information of innovative
images to get the final resultant image. Other promi-
nently, in these domains usually engages continuous
real-time monitoring.
(2) Imperfect environmental conditions The major dif-
ficulty in this field, the images may be acquired at
imperfect circumstances. Due to the weather and
enlightenment condition, the input images may contain
under-exposure and serious noise. Fusion of visible and
infrared image is shown in Fig. 6a, b. In this outline,
the fusions of both images are achieved by the guided
filtering and image statistics.
5.4 Photography Domain Applications
Figure 7 shows examples of IF in the photography domain,
the fusion of multi-focus images. It is not possible for all
objects with diverse distances from camera due to its
restricted depths to be all-in-focus within a single shot of
cameras. Due to the restricted depths of the camera, it is
not possible to be all-in-focus within a single shot of cam-
eras for all objects with diverse distances. To overcome
this, the multi-focus IF method is used to merge several
images with a similar scene having diverse focus points for
generating an all-in-focusresultant image. This resultant
compound image can well defend the significant Informa-
tion from the source image. It is more desirable in several
image processing tasks and machine vision. In Fig. 8, the
data sources used in the photography domain. The various
challenges which are faced in this domain are:
(1) Effect of moving target objects In this domain, multi-
exposure and multi-focus images are constantly pro-
vided by diverse times. In these circumstances, dur-
ing the capturing process moving objects may become
visible in diverse locations. The moving objects might
produce inconsistencies into the fused image.
(2) Relevance in consumer electronics In this, images are
taken from numerous shots with diverse settings of the
camera. The challenge is to combine the multi-exposure
and multi-focus IF methods into consumer electronics
to produce a compound image of high quality in real-
time. IF of multi-focus images (Back-focus image and
Fore-focus image) is shown in Fig. 7a, b. In this outline,
IF of multi-focus images is achieved by guided filtering
based technique and image.
5.5 Applications in Other Domains
Many other applications that are used for fusion like object
recognition, tracking, object detection etc.
Fig. 7 Examples of IF in photography domain. a Back-focus Image b Fore-focus image c Fused image

4440 H. Kaur et al.
1 3
5.6 Recognition Application
In an image one or more objects are visible. The main aim
of recognition to recognize the objects clearly. Face recog-
nition is a major application in this domain. Recognition
algorithm used infrared and visible IF method. It has two
types to recognize the image. In the first type, first fused the
images after that recognize its objects. In the next type, it is
embedded with the reorganization process,and it isproposed
by Faten et al. It can help for improving the recognition
precision with the help of narrowband and enhancement the
fusion results.
5.7 Detection and Tracking Application
Detection and tracking are used in infrared and visible IF. It
isused in real-life applications: fruit detection, object detec-
tion. It determines the accurate place of the objects at a simi-
lar time. Detection fusion algorithm can be differentiate into
two classes, in the first class detect the objects before fusing
and in the second classes, fuse the images before detecting
objects. He at el. introduced an algorithm with multilevel
image fusion and it enhanced the target detection of the
object. Pixel and feature level image fusion isalso consid-
ered in this method and it shows the relationship between
high and low-frequency Information, which is ignored in
the wavelet transform IF. The main motive of this method
to enhance target visibility.
Target tracking algorithm is similar to the detection
algorithm. It should determine the relationship between the
frames of the target objects in a particular time sequence.
Stephen et al. introduced an enhanced target tracking
approach through visible and infrared IF by using PCA-
weighted fusion rule. In most of the algorithms: detection,
recognition and tracking are independent to each other which
is designed to recover the features or visibility of the actual
images [4].
5.8 Performance Evaluation Measure
A number of performance evaluation metrics have been
anticipated to evaluate the performances of diverse IF tech-
niques. They can be categorized as subjective and objective
assessment measures [52]. Subjective assessment measures
play important role in IF as it evaluates the fused image
quality based on human visual perception. It can compare
various fusion techniques and methods according to stand-
ards like image details, image distortion, and object compe-
tence. In the infrared and visible IF subjective evaluation is
more popular and reliable. Its disadvantages are high cost,
time-consuming, irreproducibility and human intervention.
Objective assessment is carried out to quantify the fused
image quality quantitatively. It is not biased by observers and
highly consistent with visual perception. It’s arriving from
diverse types, which are based on image gradient, similar
structure, Information theory, human visual perception and
statistics [1]. A number of metrics for quantifying the qual-
ity of fused images is presented in this survey. Some other
evaluation measures of fused images are divided into two
groups. Evaluation measures are further categorized as a
reference and non-reference evaluation measures. Evalua-
tion measures based on the reference image are given below.
i. The mean of the square error (MSE) computes the
error and the real differentiation between the ideal or
expected results [1, 147]. This metric is defined as
follow:
where A and B is the ideal and compound image
respectively that can be evaluated, i and j is the pixel
row and column index respectively, m and n is the
height and width of image implying the numeral or
pixel rows and columns respectively.
ii. The Structural similarity index metric (SSIM) quanti-
fies the similarity between one or more images. It is
designed by modeling any contrast distortion and radi-
ometric. It is a combination of the luminance image
distortion and the combination of contrast distortion,
loss correlation and structure distortion between
source images and the final image [1, 11, 147–150].
This metric is defined as follow:
where the 𝜇x average of x, 𝜇y the average of y, 𝜎2
x the
variance of x, 𝜎2
y
the variance of y, 𝜎xy the covariance
of x and y,c1 and c2 two variable to stabilize the divi-
sion with the weak denominator
iii. The peak signal to noise ratio (PSNR) is used to com-
pute the ratio of peak power and noise value power [1,
11, 147, 149, 150]. This metric is defined as follow:
Here, r indicates the peak value of the fused image.
If the PSNR value is high that means fused image
closer to the input image and less distortion in the
fusion method
iv. Erreur Relative Globale Adimensionnelle de Syn-
these employed to quantify the image quality from the
fusion of high spatial resolution images. This method
(1)
MSE =
1
mn
m
∑
i=1
n
∑
j=1
(
Aij − Bij
)2
(2)
SSIM(x, y) =
(
2𝜇x𝜇y + c1
)(
2𝜎xy + c2
)
(
𝜇2
x + 𝜇2
y + c1
)(
𝜎2
x + 𝜎2
y + c2
)
(3)
PSNR = 10log10
{
r2
MSE
}

4441
1 3
is introduced by Lucien Wald [148, 151]. This metric
is defined as follow:
where h and l show the size of the PAN and MS
images, N indicates the total number of pixels in the
fused image. Mk is the mean radiance value of the MS
image for the Bk band [151]
v. Overall cross entropy (OCE) is used to determine the
difference between one or more source images to get
a fused image. If the OCF value is smaller, it means it
provide better results [11, 12]. This metric is defined
as follow:
Here CE indicates the cross-entropy of the images, it
effects the entropy of two source images fA and fB and
F is fused image
vi. Visual Information fidelity (VIF) is used to measure
the distortions of images. It includes blur, local or
global contrast changes and additive noises [1, 150].
This metric is defined as follow:
vii. Mutual Information provides the Information quan-
tity detail of source images, which are merged in the
resultant image. The highest Mutual Information rep-
resents the effectiveness of the IF technique [1, 11,
141, 150, 151]. This is defined as follows
where PA(a) and PF(f) denote the marginal histogram
of input image A and the fused image is F. PA,F(a, f)
indicate joint histogram of input image A and fused
image is F. if mutual Information value is high it
means fusion performance is good
viii. Spectral Angle Mapper (SAM) calculates spectral sim-
ilar content between original and final fused images
by looking between two vectors at the angle [21]. This
metric is defined as follow:
(4)
ERGAS = 100
h
l
√
√
√
√ 1
N
N
∑
k=1
RMSE
(
Bk
)2/
(
Mk
)2
(5)
OCE
(
fA, fB;F
)
= CE(fA;F) + CE(fB;F)∕2
(6)
VIF = Distorted image information∕Reference image information
(7)
MIAF =
∑
a,f
PA,F(a, f)log
[
PAF(a, f)
PA(a)PF(f)
]
(8)
α = cos−1
⎛
⎜
⎜
⎜
⎜
⎝
∑b
i=1
tiri
�∑b
i=1
t2
i
�1∕2
�∑b
i=1
r2
i
�1∕2
⎞
⎟
⎟
⎟
⎟
⎠
Here, b is denoted the number of bands and ti and ri
stand for an
ith
band of test and reference images
ix. Signal to noise Ratio (SNR) is used to determine the
noise. The larger the signal to noise value enhanced
the resultant compound image [11, 12]. This metric is
defined as follow:
where Ir(x, y) indicates the intensity of the pixel of the
estimated image and If (x, y) indicates the intensity of
the pixel of the source image. If the signal to noise
value is high it means estimation error is small and
better performance in the fusion.
Non-reference image quality evaluation measures
which do not need reference image are given below.
i. Standard Deviation (SD) spread the data in the whole
image [1, 151]. This metric is defined as follow:
where
−
Hdenotes the mean value of fused image. If the
value of SD is high that means fused images achieve a
good visibility effect.
ii. Spatial Frequency error (SFE) is a quantitative meas-
ure to objectively assess the superiority of the final the
fused image [149]. This is defined as follow:
where SFf-SF of the fused image, SFr-SF of the refer-
ence image.
iii. Entropy (EN) is used to evaluate the Information
content of an image and it produce sensitive noise in
the image. Image with large Information content has
low cross entropy [1, 11, 12, 149, 151]. This metric is
defined as follow:
where, L is denotes the total number of gray levels
and pl denotes the normalized histogram of the cor-
responding gray level in the IF. If the EN value is
higher it means it contains more Information and bet-
ter performance in the image fusion.
iv. The universal image quality index (UIQI) is motivated
by Human Visual System. It is based on the structural
(9)
SNR = 10log10
� ∑P
x=1
∑Q
y=1
�
Ir(x, y
�
)2
∑P
x=1
∑Q
y=1
�
Ir(x, y
�
− If (x, y))2
�
(10)
SD =
√
√
√
√
m
∑
i=1
n
∑
j=1
(
h(i, j) − H
)2
(11)
SFE =
SFf − SFr
SFr
(12)
EN = −
L−1
∑
l=0
pllog2pl

4442 H. Kaur et al.
1 3
Information of final fused resultant images with the
combination of loss of correlation, contrast distor-
tion, and luminance distortion [11, 150]. This metric is
defined as follow:
where 𝜎 is variance, 𝜇 is the average.
v. Fusion mutual Information metric evaluates the degree
of dependence of the one or more images. It is based
on Mutual Information (MI) and measures the feature
Information which is transformed from input image to
fused image. It enhanced the image quality [1, 11]. This
where A, B are input images and F is the fused image. If
FMI value is high it indicates that considerable Informa-
tion transfer from input to the fused image.
vi. Spatial frequency (SF) is an image quality index it’s
called spatial row frequency (RF) and column frequency
(CF) based on horizontal and vertical gradients. The
Spatial frequency evaluation metric can calculate the
gradient distribution of an image effectively and it gives
more texture detail of the image [1, 11, 150, 151]. This
where F is a fused image. A fused image with high SF
contain the rich edges and textures Information.
vii. The large Mean gradient (MG) measurement implies
that the composite image capture the rich edges and tex-
tures Information. Its fusion performance is better [1,
149]. This metric is defined as follow:
where F is a final fused image.
(13)
UIQI =
𝜎I1IF
𝜎I1×𝜎IF
.
𝜇I1IF
(
𝜇I1
)2
+
(
𝜇I1
)2
.
2𝜎I1×𝜎IF
(
𝜎I1
)2
+
(
𝜎I1
)2
(14)
FMI = MIA,F + MIB,F
(15)
SF =
√
RF2 + CF2
(16)
RF =
√
√
√
√
M
∑
i=1
N
∑
j=1
(F(i, j) − F(i, j − 1))2
(17)
CF =
√
√
√
√
M
∑
i=1
N
∑
j=1
(F(i, j) − F(i − 1, j))2
(18)
MG =
1
(M − 1)(N − 1)
×
M−1
∑
x=1
N−1
∑
y=1
√
((F(x, y) − F(x − 1, y))2
+ (F(x, y) − F(x, −1y))2
)/
2
ix. Average Difference (AD) is the propositional value
of the differentiation between the actual or ideal data.
[147]. This metric is defined as follow:
ix. Average Gradient (AG) measurement is used to measure
the gradient Information of the composite image. It pro-
vides the texture detail of an image [1, 150, 151]. The
AG metric is defined as follow:
If AG metric value is high that means it contains more
gradient Information and better performance in the fused
algorithm.
x. Normalized cross correlation (NCC) is employed to
determine similar content between input and fused
image [147]. This metric is defined as follow:
xi. Mean absolute error (MAE) of the related pixels in origi-
nal and final fused images [11]. This is defined as fol-
low:
xii. Normalized Absolute Error (NAE) is a quality meas-
ure that normalized the error value with respect to the
expected or ideal value. It isprovide the dissimilarity
between the actual and desired outcome which is further
divided bythe sum of the expected values [147]. This
xiii Correlation determines the correlation between refer-
enced and resultant image. If the reference image and
resultant image value are one it means both images are
exactly the same and if it is less than one it means both
images have more dissimilarity [11].
(19)
AD =
1
mn
m
∑
i=1
n
∑
j=1
|
|
|
(
Aij − Bij
)|
|
|
(20)
AG =
1
MN
M
∑
i=1
N
∑
j=1
√
∇F2
x(i, j) + ∇F2
y(i, j)
2
(21)
NCC =
∑m
i=1
∑n
j=1
�
Aij*Bij
�
∑m
i=1
∑n
j=1
�
A2
ij
�
(22)
MAE =
1
N2
M
∑
i=1
N
∑
j=1
s(i, j) − y(i, j)
(23)
NAE =
∑m
i=1
∑n
j=1
�
�
�
�
Aij − Bij
��
�
�
∑m
i=1
∑n
j=1
�
Aij
�

4443
1 3
6 Discussion
Despite the various constraints which are handled by
several researchers, still number of research and devel-
opment in the field of image fusion is growing day by
day. Image fusion has several open-ended difficulties in
different domains. The main aim is to discuss the current
challenges and future trends of image fusion that arise
in various domains, such as surveillance, photography,
medical diagnosis, and remote sensing are analyzed in
the fusion processes. This paper has discussed various
spatial and frequency domain methods as well as their
performance evaluation measures. Simple image fusion
techniques cannot be used in actual applications. PCA,
hue intensity saturation and Brovey methods are compu-
tationally proficient, high-speed and extremely straight-
forward but resulted in distortion of color. Images fused
with Principal component analysis have a spatial advan-
tage but resulted in spectral degradation. The guided fil-
tering is an easy, computationally efficient method and is
more suitable forreal-world applications. The number of
decomposition levels affects the pyramid decomposition
in image fusion outcome. Every algorithm has its own
advantages and disadvantages. The main challenge faced
in remote sensing field is to reduce the visual distortions
after fusing panchromatic (PAN), hyperspectral (HS) and
multi-spectral (MS) images. This is because source images
are captured using different sensors with similar platform
but do not focus on a same direction as well as their gain-
ing moments are not exactly the same. The dataset and its
accessibility represent a restriction that is faced by many
researchers. The progress of image fusion has increased its
interest in colored images and its enhancement. The aim
of color contrast enhancement is to produce an appealing
image with bright color and clarity of the visual scene.
Recently, researchers have used neutrosophy in image
fusion, used to remove noise and to enhance the quality
of single photon emission tomography (SPET), computed
tomography (CT), magnetic resonance imaging (MRI),
and positron emission tomography (PET) image. This
integration of neutrosophy with image fusion resulted in
noise reduction and better visibility of the fused image.
Deep learning is the rising trend to develop the automated
application. It extremely applied in various applications
such as face recognition, speech recognition, object detec-
tion and medical imaging. The integration of quantitative
and qualitative measures is the accurate way to determine
which particular fusion technique is better for certain
application. The various challenges which generally are
faced by researchers is to design image transformation and
fusion strategies. Moreover, the lack of effective image
representation approaches and widely recognized fusion
evaluation metrics for performance evaluation of image
fusion techniques is also of great concern. Whereas the
recent progresses in machine learning and deep learn-
ing based image fusion shows a huge potential for future
improvement in image fusion.
7 Conclusion
Recently, the area of image fusion is attracting more atten-
tion. In this paper, various image fusion techniques with
their pros and cons, different methods with state-of-art has
been discussed. Different applications like medical image,
remote sensing, photography and surveillance images have
been discussed with their challenges. Finally, the different
evaluation metrics for image fusion techniques with or with-
out reference has been discussed. Therefore, it is concluded
from survey that each image fusion technique is meant for a
specific application and can be used in various combinations
to obtain better results. In future, new deep neural networks
based image fusion methods will be developed for various
domains to improve the efficiency of fusion procedure by
implementing the algorithm with parallel computing unit.
Compliance with Ethical Standards
Conflict of Interest There is no conflict of interest.
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