An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6361~6368
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6361-6368  6361
Journal homepage: http://ijece.iaescore.com
An underwater image enhancement by reducing speckle noise
using modified anisotropic diffusion filter
Venkatesan Malathi1
, Arumugam Manikandan2
1
Department of Computer Science, Periyar University, Salem, India
2
Department of Computer Science, Muthayammal Memorial College of Arts and Science, Salem, India
Article Info ABSTRACT
Article history:
Received Oct 17, 2022
Revised Mar 18, 2023
Accepted Apr 7, 2023
Underwater images are usually suffering from the issues of quality
degradation, such as low contrast due to blurring details, color deviations,
non-uniform lighting, and noise. Since last few decades, many researches are
undergoing for restoration and enhancement for degraded underwater
images. In this paper, we proposed a novel algorithm using modified
anisotropic diffusion filter with dynamic color balancing strategy. This
proposed algorithm performs based on an employing effective noise
reduction as well as edge preserving technique with dynamic color
correction to make uniform lighting and minimize the speckle noise.
Furthermore, reanalyze the contributions and limitations of existing
underwater image restoration and enhancement methods. Finally, in this
research provided the detailed objective evaluations and compared with the
various underwater scenarios for above said challenges also made subjective
studies, which shows that our proposed method will improve the quality of
the image and significantly enhanced the image.
Keywords:
Anisotropic diffusion filter
Contrast limited adaptive
histogram equalization
Dark channel prior
Dynamic color correction
Image enhancement
Speckle reduction anisotropic
diffusion
Underwater
This is an open access article under the CC BY-SA license.
Corresponding Author:
Venkatesan Malathi
Department of Computer Science, Periyar University
Salem, India
Email: malathikb@gmail.com
1. INTRODUCTION
Since last few decades, huge research is undergoing for the development of color images-based
application in various fields like medical, and security application in defense. It has in the urge of exposure of
effective tools and algorithms for color image processing. Even though variety of researches is focusing
about image processing, the research in underwater image is not in considerable amount of attention given.
The environment of underwater is very complex and as source of light in underwater environment is
non-uniform or some places, the presence of light is absence, due to these various complex difficulties,
underwater imaging systems have to require on the light to provide illumination artificially [1]. Figure 1
explains an illustration of underwater image capturing system.
The enormous researches are show that underwater images have various challenges and forces
significant problems due to reflection, absorption bending and scattering, poor visibility [2]. In this research,
a proposed method for restoration and enhancement of underwater images is proposed. In this proposed
approach, considerable improvement of image restoration and enhancement is using edge preserving
technique with the help of gamma correction and dynamic color correction techniques. The rest of this paper
organized as follows. In section 2, the existing and related works of underwater image processing techniques
are discussed; section 3 details the motivation of the proposed method and section 4 shows the results of
qualitative and quantitative comparison of the proposed method with state-of-the arts methods. Finally,
summarizes the conclusion and discussed the further work of the study.

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Figure 1. Illustration of underwater image capturing system
2. RELATED WORK
Last few years, enormous research has been going on for image restoration and enhancement. Image
restoration is dealt with in an objective manner and it is related to feature extraction from the imperfect image
and the function of image enhancement is kind of subjective [3]. It could not be precisely represented by
mathematical function and also it is manipulated the degraded image, improves the contrast of the image and
visual appearance could be improved. In this paper, various existing image restoration and enhancement
methods are compared with the proposed method. Han et al. [4] proposed the simplest and most efficient prior,
called dark channel prior (DCP), for the application of single image haze removal. This algorithmic depends
on the statistical modeling of the outdoor images and while implementing this prior into the haze imaging
model, it is observed that removal of single image haze becomes more effective and simpler. In this method, at
the first estimating the transmission map, then applied the soft matting algorithm for the purpose to refine the
transmission. Hou et al. [5] presented an underwater color image enhancement approach named wavelet-
domain filtering and constrained histogram stretching algorithms (WDF–CHS) based on H preserving.
Fu et al. [6] presented and addressed mainly two challenges to enhance underwater image quality.
Initially, to address the color distortion based on piece-wise linear transformation, they were introduced an
effective color correcting strategy. Also, they were proposed a novel optimal contrast improvement method
to address the low contrast, it is efficient and may reduce artifacts. In this paper, authors were addressed color
shift and low contrast as ed issues by two-step image enhancement procedure for single underwater images.
Also, the authors show proved that the proposed method was well suitable for real-time applications.
To restore and enhance underwater images, with aid of image formation model (IFM), Peng et al.
[7] proposed a depth estimation method for underwater scenes based on image blurriness and light
absorption. Previous IFM-based image restoration methods are estimated the scene depth based on the DCP
or the maximum intensity prior (MIP). It leads to poor restoration results. Based on both image blurriness and
light absorption method, Balaji et al. [8] proposed a new restoration method, in this proposed method,
efficient BL and depth estimation were provided. The Authors proved that their proposed method was
produce better restoration output by both the subjective and objective experimental.
Ancuti et al. [9] described a novel method for underwater videos and image enhancement. Using the
fusion principles, this method obtained by the weight measures from the degraded version of the image. To
retrieve underwater images, Schettini and Corchs [10] proposed a red channel method, in this, the colors
combined with short wavelengths are recovered, as it is expected for underwater images, and leads to a
recover the lost contrast. This method was used for retrieve the images which degraded by the atmosphere
mostly affected by haze. Nuclei segmentation and optimized classification with deep learning approach
features classification of the forecasted nucleus for reach accuracy [11]. Image retrieval approach that applies
locality-sensitive hashing with convolutional neural networks to extract several feature types. This approach
concentrates on both the high-level and low-level, which offers visual content of the images [12]. The
artificial neural network is utilized to precisely notice the mass lesions in the mammogram images in a short
time [13]. The object-based classification method demonstrates how the object-based method can be
employed in the available data to precisely realize vegetation that can be sub-categorized to receive region
under tree canopy [14].
3. PROPOSED METHOD
In this research, the proposed method is used to restore and enhance the visibility and quality of the
underwater image. The block diagram for the proposed method is shown in Figure 2. Since the underwater

Int J Elec & Comp Eng ISSN: 2088-8708 
An underwater image enhancement by reducing speckle noise using modified … (Venkatesan Malathi)
6363
image is generally degraded due to particles present in the medium and it is lead to non-uniform illumination
of light and it will affect the quality and visibility of the images causing poor contrast and color retention.
Initially, the underwater input image is pre-processed by converting red, green, and blue (RGB) into gray
images then color compensation and color corrected algorithm, then it is followed by gamma correction and
white balancing. This filtered output image is enhancing the contrast with high-frequency components due to
non-uniform illumination in RGB underwater input image using speckle reducing anisotropic diffusion
(SRAD) filter. SRAD filtered output is again processed with a color compensation and white balancing for
getting a better pleasant visual effect. The system model for the proposed algorithm is shown in Figure 2. It
describes the complete flow of the proposed algorithm. The following section is discussed the preprocessing
stages of color correction and white balancing process. After the preprocessing step, modified speckle
reduction anisotropic diffusion (MSRAD) filter is used to reduce the speckle noise after that image.
Figure 2. System model of the proposed work
3.1. Evaluation of pre-processing image
In this stage, before applying denoising algorithm, need to pre-process input image by using color
compensation, gamma corrections and white balancing.
a. Color compensation
Color compensation is used as initial pre-processing step. In this step, the mean of each channel and
gray mean value are determined and then calculate individual value of each channel is calculated by using the
mean value of gray image and its own channel mean value and then color correction is done for red and blue
channel by adjusting the α value from 0 to 1 (considered as 0.3). Determining individual color channel value
then make all channels have same mean color correction made for red and blue channel.
𝐼𝑟 = 𝐼𝑟 − 0.3 ∗ 𝑚(𝐼𝑚𝑔) − 𝑚(𝐼𝑚𝑟) ∗ 𝐼𝑔 ∗ (1 − 𝐼𝑟) (1)
𝐼𝑏 = 𝐼𝑏 + 0.3 ∗ 𝑚(𝐼𝑚𝑔) − 𝑚(𝐼𝑚𝑏) ∗ 𝐼𝑔 ∗ (1 − 𝐼𝑏) (2)
Where, 𝐼𝑟 is red channel of the input image, 𝐼𝑏 is blue channel of the input image, 𝐼𝑔 is green channel of the
input image, 𝐼𝑚𝑏 is mean value of the blue channel, 𝐼𝑚𝑟 is mean value of the red channel, and 𝐼𝑚𝑔 is mean
value of the green channel
b. Gamma correction
Gamma correction or gamma is a nonlinear operation and it is used to encode and decode luminance
in video or still image systems. Using power law, it is given by (3),
𝑉𝑜𝑢𝑡=𝐴𝑉
𝑖𝑛
γ
(3)
where the positive real input value 𝑉𝑖𝑛 is maximized to the power 𝛾 and multiplied by the constant A to get
the output value 𝑉𝑜𝑢𝑡. If A=1, inputs and outputs values are lies between in the range 0–1. If gamma (𝛾) is
less than 1, then is denoted as an encoding gamma, and the process of encoding with this compressive power-
law nonlinearity is called gamma compression; if gamma (𝛾) is greater than 1, than it is denoted as a
decoding gamma, and the application of the expansive power-law nonlinearity is called gamma expansion.

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c. White balancing
The color casting may be introduced in the captured images. A method for identifying an
independent color of light is called color constancy. To solve this particular issue, the existing light will be
taken out and its component colors estimated. In general, gray-world and Max-RGB algorithms estimate the
color of the light The gray-world algorithm, a white balance technique used in this study, assumes that the
input image is typically neutral grey. Using this process, one can estimate the lighting color cast by looking at
the average color and comparing it to grey. By calculating the mean of each image channel, the Grey World
method generates an estimate of lighting. To normalize the underwater image of channel i, the pixel value of
the image is scaled by (4),
𝑆1 =
𝑎𝑣𝑔
𝑎𝑣𝑔𝑖
(4)
where, 𝑎𝑣𝑔𝑖 is the channel mean and avg is the illumination estimate.
3.2. Anisotropic diffusion filter to reduction speckle noise
By using a partial differential equation (PDE) technique, incorporated in the SRAD filter, it is
possible to eliminate the speckle noise present in an image. Even the most basic anisotropic diffusion filters
can perform edge sensitive diffusion for anisotropic diffusion, developed by Rahman et al. [15], is thought to
be the edge-sensitive extension of the average filter, as opposed to SRAD, which is thought to be the edge-
sensitive extension of the adaptive speckle filter. The SRAD filter uses a diffusion technique based on the
minimum mean square error (MMSE). Since Lee filter and Frost filter also employ this strategy, their results
are comparable. Compared to traditional anisotropic diffusion, anisotropic diffusion in SRAD filters is
unique and advantageous. Even though it operates in the typical manner at the edge's center, it nonetheless
affects negative edge diffusion on both sides of the edge. As a result, the edge's contour is sharper, resulting
in darker and brighter than usual sides of the edge [16].
{
𝜕𝐼
𝜕𝑡
= 𝑑𝑖𝑣[𝑐(𝑞). 𝛻𝐼]
𝐼(𝑡 = 0) = 𝐼0.
(5)
By applying Jacobi iterative method over the (3) and (6) obtain:
𝐼𝑖,𝑗
𝑛+1
= 𝐼𝑖,𝑗
𝑛
+ (
𝛥𝑡
4
) 𝑑𝑖,𝑗
𝑛
. (6)
here, the definition of diffusion coefficient 𝑐(𝑞) is:
𝑐(𝑞) = 𝑒𝑥𝑝 {−
[𝑞2−𝑞0
2]
[𝑞0
2(1+𝑞0
2)]
} (7)
𝑞 = √
(
1
4
)(
|𝛻𝐼|
𝐼
)
2
−(
1
42)(
|𝛻2𝐼|
𝐼
)
2
⌈1+(
1
4
)(
|𝛻2𝐼|
𝐼
)⌉
2 (8)
4. RESULTS AND DISCUSSION
Full reference-based quality measurements result in unsuitable subjective evaluation because the
peak signal to noise ratio (PSNR) is always lower in underwater image scenes due to general scene distortion.
Consequently, in this study, a no-reference based quantitative evaluation measure of distorted underwater
image scenes was taken into consideration [17]. In this research, considered six of the following parameters
such as average gradient, entropy, edge intensity, patch-based contrast quality index (PCQI), underwater
image and quality measure (UIQM) and underwater color image quality evaluation metric (UCIQE) are used
to demonstrate the performance and advantages attained by our proposed method [18]. The UIQM and
UCIQE are no-reference parameters and PCQI is with reference parameter.
The proposed method is compared with the state-of-the-art work via above mentioned metrics for
the images shown in Figures 3 to 5 and corresponding comparison data values are shown in Table 1 to 3. In
this proposed work, the underwater image enhance benchmark (UIEB) images [19] are taken for quantitative

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experimental analysis. In this research considered five UIEB images and all these images are enhanced with
our proposed method and then compared their performances with various existing methods [20]–[24] using
above mentioned performance metrics.
Figure 3. UIEB images for quantitative experimental analysis comparison of our proposed method with state-
of-the-art work
Figure 4. UIEBC images for quantitative experimental analysis comparison of proposed method with state-
of-the-art work

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Figure 5. RUIE images for quantitative experimental analysis comparison of our proposed method with state-
of-the-art work
Table 1 describes the average values of the proposed and existing algorithms for various
performance metrics like UIQM, UCIQE, PCQI, average gradient, edge intensity and entropy for UIEB
images of proposed and existing algorithm [25], [26]. From this table, it is observed that, proposed method
being produce better performance in all metric except PCQI values. In this work, used no-reference data, due
to this getting UIQM and UCIQE are good enough values and PCQI has poor response.
Table 1. Average value of UIEB dataset
UIQM UCIQE PCQI Average Gradient Edge Intensity Entropy
DCP 1.21518 0.531764 0.971588 3.4552224 36.04665252 7.159062
FUSION 1.251124 0.580736 1.125603 4.7669431 50.04985942 7.635385
ARC 1.031423 0.516431 1.070736 3.1056281 32.59000628 7.230595
UIBLA 1.049677 0.5058 1.137428 3.7888624 39.71627794 7.526755
TWO STEP 1.1847 0.497278 1.182411 4.2883913 44.85363493 7.114091
GDCP 1.111356 0.499796 1.056433 4.1448251 43.33681959 7.169811
HUE 1.246339 0.602754 0.962365 4.3597861 45.30746406 7.702979
PROPOSED 1.303056 0.661121 1.029761 4.8443004 52.69452947 7.652017
Rank 1 1 6 1 1 2
Figure 4 shows that underwater image enhancement benchmark challenged (UIEBC) image dataset
for subjective analysis of proposed method with existing algorithm. The objective values for proposed and
existing methods are given in the Table 2. From the table, it is observed that the proposed method gives
better performance than that of DCP, Fusion, ARC and UIBLA methods. In this research, challenged - No
reference images are used for analysis, due to this, the objective values of Average gradients, entropy of the
proposed method value is lesser than the existing method.
Figure 5 shows that real-world underwater image enhanced (RUIE) image dataset for the subjective
analysis of proposed method with existing algorithm. The objective values for proposed and existing methods
are given in the Table 3. The Table 3 have the average values of various performance metrics like UIQM,
UCIQE, PCQI, Average gradient, edge intensity and entropy for RUIE images of proposed and existing
algorithms. From this table, it is observed that the proposed method gives better performance except PCQI

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values. Rank for all existing and proposed method is given for all performance metrics for quick observation.
It is observed that, the entropy of UIEB and RUIE dataset is higher than the other methods. But, in UIEBC, it
ranks 5, due to the usage of challenges - No reference images.
Table 2. Average value of UIEBC dataset
UIQM UCIQE PCQI Average Gradient Edge Intensity Entropy
DCP 1.108635 0.506508 0.908895 1.998555 21.15608 6.220873
FUSION 1.136368 0.594616 0.992773 3.684717 38.70833 7.224158
ARC 0.975017 0.521074 0.921482 2.098793 22.31972 6.585772
UIBLA 1.209401 0.61141 1.003758 3.740324 39.73055 7.098359
TWO STEP 1.145304 0.584047 1.040184 4.597862 48.86106 7.449934
GDCP 1.184508 0.599683 0.981126 4.147914 43.92099 7.251545
HUE 1.200059 0.585642 0.884571 3.14661 32.86251 7.269209
PROPOSED 1.183254 0.663582 0.969419 3.949454 42.99054 7.077169
Rank 4 1 4 3 3 5
Table 3. Average value based on RUIE dataset
UIQM UCIQE PCQI Average gradient Edge intensity Entropy
DCP 1.165308 0.518504 0.996152 2.407795 26.17732 7.159754
FUSION 1.163584 0.598194 1.173877 4.244993 46.24449 7.710271
ARC 0.959382 0.54227 1.084915 2.752395 29.96073 7.330692
UIBLA 0.928843 0.49605 1.104862 2.596007 28.20542 7.353155
TWO STEP 1.082002 0.521306 1.215046 3.825236 41.58739 7.222659
GDCP 1.184953 0.541974 1.057842 3.179214 34.48807 7.205088
HUE 1.108178 0.597427 0.991549 3.701663 40.28211 7.752023
PROPOSED 1.289912 0.68392 1.119634 4.685068 51.5409 7.817841
Rank 1 1 3 1 1 1
5. CONSLUSION
In this proposed method, the enhancement of underwater image is improved by employing the
MSRAD filter. In this proposed method, the captured image is pre-processed with various stages like white
balancing, and color correction, for improvement of underwater images visual effect. The pre-processed
image is still enhanced with MSRAD filter. In this research, UIEB, UIBC and RUIE image data set are used
for the purpose of subjective analysis. In UIEB and RUIE proposed methods performance are better than the
Hue and Fusion method, the performance are same only in UIEBC because we used challenged-No-reference
images. This proposed method performance is compared with various existing method like DCP, Fusion,
ARC, UIBL, two step algorithm and GDCP algorithm. From the performance metric values, it is observed
that, our proposed method will enhance the hazy underwater images better than existing methods. Further,
this work can be extended using convolutional neural network for better underwater image enhancement.
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BIOGRAPHIES OF AUTHORS
Venkatesan Malathi received her bachelor’s Degree in 2000 and Master degree
in 2004 from University of Madras, Tamilnadu, India and M.Phil. from Periyar University,
Tamilnadu, India in 2009. She currently pursuing Ph.D at Periyar University. She has 10 years
of teaching experience. Currently, she is working as an Assistant Professor, SRM
Arts Science College, Chennai, Tamilnadu, India. Her research interests are in image
processing, wireless sensor networks and network security. She also published her
research works in various international journals and presented in many national and
international conferences. She can be contacted at email: malathikb@gmail.com.
Arumugam Manikandan received bachelor’s Degree in 1997, Master degree in
1999 from Bharathidasan University, Tamilnadu and M.Phil. from Manonmaniam
Sundharanar University, Tamilnadu in 2003. He finished MCA from Periyar University in
2008. He completed M.Tech in 2011 from Prist University, Tamilnadu. He did his Ph.D. in
2017 at Dravidian University, Andra Pradesh. He has 21 years of teaching experience.
Currently, he is working as a Principal and Assistant Professor for the past 5 years at
Muthayammal Memorial College of Arts and Science, Rasipuram, Tamilnadu, India. His
research interests are in computer networks, wireless networks, network security, image
processing and data mining. He is a life member of Indian science congress association
and Indian society for technical education. He published 29 national and
international journals and also published 5 books of programming in C, embedded
systems, web technology, computer programming and object modeling using UML.
He is being guided 4 research scholars. He is a board member of various journals and
also member in professional bodies of TNSRO, IARA, and TERA. He can be
contacted at email: s.a.manikandan@gmail.com.

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

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