IRJET- Contrast Enhancement of Grey Level and Color Image using DWT and SVD

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 291
Contrast Enhancement of grey level and color image using
DWT and SVD
K. Siva Leela1, P. Surendra Kumar2, D. Swetha3, K. Kalyan4, K. Yaswika5, K. Anil6
1,4,5,6 B.Tech, Department of ECE, BEC, Andhra Pradesh, India
2,3 Assistant Professor, Department of ECE, BEC, Andhra Pradesh ,India
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Abstract - With the fast advance of technologies and the
prevalence of imaging devices, billions of digital images are
being created every day. Due to undesirable light source,
unfavorable weather or failure of the imaging device itself,
the contrast and tone of the captured image may not always
be satisfactory. Therefore, image enhancement is often
required for both the aesthetic and pragmatic purposes.
Contrast enhancement technique is capable to clean up the
unwanted noises and enhance the images brightness and
contrast. The proposed algorithm enhance brightness and
contrast simultaneously without changing color. This is
applicable to any format of image and of any size.
Key Words: GHE, DWT, IDWT,SVD
Contrast enhancement is frequently referred to as one of
the most important issues in image processing[8]. General
Histogram Equalization is one of the common methods
used for improving contrast in digital images[3]. The
conventional histogram equalization methods usually
result in excessive contrast enhancement, which causes
the unnatural look and visual artifacts of the processed
image[4]. The idea behind image enhancement is to bring
out the detail that is obscured and also to highlight certain
features of interest in an image[7]. Visual system is more
sensitive to contrast. Contrast of an image is determined
by its dynamic range, which is defined as the ratio
between brightest and the darkest pixel intensities[1].
There exist many methods to enhance contrast[2]. But the
proposed technique works for the quality of an image with
respect to brightness as well as contrast
simultaneously[5].
Histogram equalization is used to enhance the contrast
of the image, it spreads the intensity values over full range.
Histogram equalization technique can’t be used for images
suffering from non-uniform illumination in their
backgrounds as this process only adds extra pixels to the
light regions of the image and removes extra pixels from
dark regions of the image resulting in a high dynamic
range in the output image. The goal of histogram
equalization is to spread out the contrast of a given image
evenly throughout the entire available dynamic range. A
key advantage of this technique is that it is fairly
straightforward and effective. The calculation is not
computationally intensive. It is powerful in highlighting
the borders and edges between different objects, but may
reduce the local details within these objects, especially
smooth and small ones.
A good histogram is that which covers all the
possible values in the gray scale used. This type of
histogram suggests that the image has good contrast and
that details in the image may be observed more easily.
DWT provides multi resolution representation of image
and can efficiently implemented using digital filters. Image
itself is considered as two dimensional signal. When image
is passed through series of low pass and high pass filters,
DWT decomposes the image into sub bands of different
resolutions. An image is decomposed into four sub-bands
denoted LL, LH, HL, and HH at level 1 in the DWT domain,
where LH, HL and HH represent the detail wavelet
coefficients and LL stands for the coarse coefficients. High
frequency sub-band contains high frequency component, it
contains edge information so can be used for increasing
resolution. Where LL sub-band is nothing but low
resolution of original image which contains illumination
information so can be used for enhancing contrast.
Singular Value Decomposition method can transform
matrix A into USVT product which allows us to refactoring
of a digital image into three matrices. The using of singular
values of such refactoring allows us to represent the image
with a smaller set of values, which can preserve useful
features of the original image, but use less storage space in
the memory. The singular value matrix represents
intensity information of a given image and any image into
three matrices. The using of change on singular values
change intensity of input image, hence other information
in the image will not be changed. Singular Value
decomposes an image into three matrices. Singular value
matrix 'S' obtained by SVD contains the illumination
information. SVD of an image, which can be interpreted as
matrix, is written as follows:
A=USVT (1.1)
Where, U and V are orthogonal square matrices known as
hanger and aligner, respectively. S Matrix contains the
sorted singular values on its main diagonal. The idea of
1.2 DWT
1.1GHE
1.3 SVD
1. INTRODUCTION

using SVD for equalizing image comes from the fact that S
contains intensity information of a given image.
2. PROPOSED ALGORITHM
Fig – 1: Detailed steps of proposed algorithm
Step 1: Applying GHE
The input image I is first processed by using GHE to
generate Î.
Step 2: Applying DWT
Both of these images I and Î are transformed by DWT into
four sub-bands. Using LL sub-bands obtained after
applying DWT, correction coefficient is to be obtained by
the following equation.
(∑ ̂)
(∑ )
(2.1)
Where, ΣLL Î is the LL singular value matrix of output of
GHE and ΣLL I is the LL singular value matrix of input
image.
Step 3: Finding new LL
New LL of an image is composed by multiplying correction
factor with ΣLL I
Now, the new LL sub-band that is obtained and higher
sub-bands of DWT are then recombined by applying
Inverse Discrete Wavelet transform to generate resultant
equalized image Î.
And thus obtained image is enhanced for both contrast as
well as brightness.
Performance is analyzed through different quality
measures.
2.1 Mean
Mean is the average of pixel values. Mean is
required to calculate standard deviation which is the
quality measure for contrast enhancement. And it is given
as
(2.2)
2.2 Standard Deviation
Standard Deviation is calculated using mean
value. Standard Deviation value gives the deviation of
pixel from its mean value i.e. deviation value tells us
spread between the pixels. Spreading between the pixels is
used to check its contrast enhancement, as if pixels are
distributed along grey levels equally then
that image is said to be highly contrast image. It is given
as:
√
∑
(2.3)
2.3 PSNR
Peak signal to noise ratio, often abbreviated PSNR,
is an engineering term for the ratio between the maximum
possible power of a signal and the power of
corrupting noise that affects the fidelity of its
representation. Because many signals
have a very wide dynamic range, PSNR is usually
expressed in terms of the logarithmic decibel scale.
PSNR is most easily defined via the mean squared
error (MSE). Given a noise-free m×n monochrome
image I and its noisy approximation K, MSE is defined as:
∑ ∑ [ ] (2.4)
The PSNR (in dB) is defined as:
( ) (2.5)
Fig 3.1.1: Sun image before DWT-SVD algorithm
3. Experimental Results

Fig 3.1.2: Sun image after DWT-SVD algorithm
Fig 3.2.1: Grass image before DWT-SVD algorithm
Fig 3.2.2: Grass image after DWT-SVD algorithm
Table 3.1: Mean and PSNR for the images
Image
Mean of
the input
image
Mean of
the output
image
PSNR for the
proposed
technique (dB)
Sun 103.9569 139.5868 24.0799
Fly 108.0994 139.8722 24.0654
Grass 32.0569 84.9608 27.3497
Table 3.2: Standard Deviation for the images
Image
Standard
Deviation of the
input image
Standard
Deviation of the
output image
Sun 84.8888 114.0965
Fly 121.3856 157.5604
Grass 21.9057 47.2239
4. CONCLUSION AND FUTURE SCOPE
In this paper, a new image contrast enhancement
technique based on DWT and SVD has been proposed. This
technique decomposed the input image into the DWT sub
bands, and after updating the singular value matrix of the
LL sub band, it reconstructed the image by using IDWT.
The proposed technique was compared with GHE
techniques for visual and quantitative performance
evaluation. The quantitative results supports the visual
results that the quality and information content of the
equalized images are better preserved through the
proposed DWT and SVD technique over GHE techniques.
DWT and SVD technique enhance contrast as well as
brightness and also enhance color images. This will be
applicable to all types of images and any size. In future, we
will design the algorithms with simple functions and get
the enhanced output quickly.
REFERENCES
[1] Pooja Bidwai and D.J.Tuptewar, "Resolution and
contrast enhancement of grey level, color image and
satellite image," published in Information Processing
(ICIP), 2015 International Conference on 16-19 Dec. 2015
[2] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda,
“Photographic tone reproduction for digital images,” in
Proc. SIGGRAPH Annu. Conf. Comput. Graph., Jul. 2002, pp.
249– 256.
[3] Y. Kim, “Contrast enhancement using brightness
preserving bi-histogram equalization,” IEEE Trans.
Consum. Electron., vol. 43, no. 1, pp. 1–8, Feb.1997.
[4] Demirel Hasan, and Gholamreza Anbarjafari, "Image
resolution enhancement by using discrete and stationary
wavelet decomposition," Image Processing, IEEE
Transactions on 20.5 (2011): 1458-1460.
[5] Darshana Mistry, Asim Banerjee, “Discrete Wavelet
Transform using Matlab,” International Journal of
Computer Engineering and Technology (IJCET) Volume 4,
Issue 2, March – April (2013), pp. 252-259
[6] Swati D. Birare, Dr. sanjay Nalbalwar,
“Review on super resolution of images using wavelet
transform,” International journal of engineering science
and technology, vol.2 (12),2010
[7] Richard E. Woods, R.C. Gonzalez, “Digital Image
Processing, 3rd edition 2008.”
[8] Hasan demirel, cargi ozcinar, and gholmreza
anbarjafari, “Satellite Image Contrast Enhancement using
DWT and SVD,” IEEE Geoscience and remote sensing
letters ,VOL.7,NO.2,April2010.

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