IRJET-Underwater Image Enhancement by Wavelet Decomposition using FPGA

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 06 | June -2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1344
UNDERWATER IMAGE ENHANCEMENT BY WAVELET DECOMPOSITION
USING FPGA
Venktesh R Kawle1, A. M. Shah2
1M. Tech Scholar, Electronics and Telecommunication Department, GCOE, Amravati (MH), India
2Assistant Professor, Electronics and Telecommunication Department, GCOE, Amravati (MH), India
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Abstract – Underwater vision is a significant issue in ocean
engineering. Atmospheric images and underwater images
undergo from medium scattering and light distortion, leading
to the low contrast and visibility of images. The problems
result in limited usage of the images. In terms of detection, the
objects in the image hardly seen and not detectable while
having a low possibility of tracing. The main purpose of
underwater image fusion is to combine multi-images about
the same object into a high-quality image with abundant
information. A Field Programmable Gate Array (FPGA) is a
reconfigurable hardware, providing better features than DSP
and other hardware devices due to their product fidelity and
sustainable advantages in digital image processing. FPGA has
a large impact on image and video processing; this is due to
the potential of the FPGA to have parallel and high
computational density as compared to a general purpose
microprocessor.The fusion of multisensory image data has
become a useful tool in underwater image application. This
paper proposes underwater images enhancement by wavelet
decomposition based image fusion implementation on FPGA.
The color corrected and contrast-enhanced images are fused
which are withdrawn from an original underwater image.
Key Words: Underwater image; Colour correction; Contrast
enhancement; Wavelet decomposition; Image fusion;
1.INTRODUCTION
Nowadays, research area trends have been increased in the
marine stream. But to work on the aquatic objects, it is
necessary to obtain the clear images of the underwater
objects. As the air interface deals with the environmental
and camera problems like dust particles, natural light,
reflection, focus and distance, underwater images also faces
the same problems. Underwater image quality depends on
density of water, depth of water, distance between camera
and object, artificial light, water particles, etc.increaseinthe
depth of water, the water becomes denser because of sand,
planktons, and minerals. As density increases, the camera
light gets deviates back and deflects by particles for some
time along the path to the camera and other part of camera
light gets absorbed by the particles. This scattering effect
causes the reduced visibility of image with low contrast.
Also, the color change effect depends on the wavelength of
light travel in the water.
Fusion is an important technique within many disparate
fields such as remote sensing, robotics, and medical
applications. Image fusion algorithm based wavelet
transform is that, the two images to be processed are
sampled to the one with the same size and they are
respectively decomposed into the sub images using forward
wavelet transform, which have the same resolution at the
same levels and different resolution among different levels;
and information fusion is performed based on the high-
frequency sub images of decomposed images;andfinallythe
resulting image is obtained using inversewavelettransform.
The result of image fusion is a single image which is more
suitable for human and machine perception or further
image-processing tasks.
Most of the image enhancement implementations found in
the literature are based on MATLABandC/C++.MATLABisa
high performance language for technical computing and the
excellent tool for algorithm development and data analysis.
Reconfigurable hardware in the form of FPGA is considered
as a practical way of obtaining high performance for
computationally intensive image processing algorithms.
FPGA’s have been traditionally configured using Hardware
Description Languages (HDL) Verilog and Very High Speed
Integrated Circuits (VHSIC) HDL (VHDL).C-basedHDL shave
also been used. Another area where research is ongoingis to
develop and employ high-level design tools which will bring
down the development time required for deploying signal
processing solutions using FPGA. Xilinx System Generator
(XSG) is one such tool that enables the use of Math works
model-based design environment Simulink for FPGAdesign.
2. LITERATURE SURVEY
We have done literature survey on the underwater image
and conclude that the hybridization of algorithms isdonefor
better visualization like wavelet fusion and contrast
enhancement, improving contrast and color correction etc.
J. Wang, et al. [1], proposed The image fusion method is
mainly divided as three ways: the first is a direct fusion
method, which is used to fuse two source images of spatial
registration into an image using some simple processions
such as direct selecting or weighted average. The second
algorithm is based on pyramid decomposition and
reconstruction, which is eventually formed through
reconstruction. The third method is the fusion algorithm
based on the wavelet transform, which fuses images
pertinently in the feature fields of each layer using multi-
resolution analysis and Mallat fast algorithm. Alex, et al. [2],
proposed on adaptive histogram equalization technique to
improve the enhancement of images. In the adaptive

histogram equalization technique, the pixels are mapped
based on it local gray scale distribution. Xiu Li, et al. [3],
proposed two parameters due to underwaterimagesquality
degraded. They proposed a novel technique based on dark
channel prior and luminance adjustment.
C. Ancuti, et al. [4], proposed Classical image enhancement
techniques have been modified to adapt to the underwater
imaging. The most popular method is underwaterimageand
video enhancement using fusion to combine different
weighted images using saliency, luminance, and
chrominance via filtering. M. S. Hitam, et al. [5], there are
many image-base methods in underwater Image
enhancement. Global and local image contrastEnhancement
is widely used to improve the appearance of underwater
images. Hitam et al., proposed a method called Mixture
contrast limited adaptive histogram equalization (clahe).
Clahe is operated on RGB and HSV color models,Andthetwo
results are combined together with Euclidean Norm.Ahmad
et al. A. S. A. Ghani and n. A. M. Isa [6], proposed a new
method called dual image Rayleigh-stretched contrast-
limited adaptive histogram Specification, which integrated
global and local contrast correction. Yafei Wang, et al. [7],
proposed fusion process involves two inputs which are
represented as color corrected and contrast enhanced
images extracted from the original underwater image. Both
the color corrected and contrast enhanced images are
decomposed into low frequency and high frequency
components by three-scale wavelet operator.
In this paper, an efficient fusion-based underwater image
enhancement approach using wavelet decomposition is
presented. The experimental results demonstrate that the
proposed approach effectively improves the visibility of
underwater images.
3. System Development
The fusion strategies used in spatial domain from the previous
different methods, a fusion-based underwater image enhancement
using FPGA is proposed in the frequency domain. Here low
frequency and high-frequency components are decomposed from
the color corrected image and contrast-enhanced image by
wavelet to employ the fusion process.
Fig.1: Proposed block diagram of underwater image
enhancement by wavelet decomposition
The proposed enhancing strategy consists of three main
steps: color correction (first input of fusion process) in
Section II-A, contrast enhancement (second input of fusion
process) in Section II-B and multi-scale fusion process for
the two inputs in Section II-C.
3.1 Preprocessing
In preprocessing, the image will be resized to the fixed
dimension. After resize of the image in this we will check the
color space of input image whether it is grey or color and
according to features extraction of preprocessing image will
be done.
Fig2: Image Pre-Processing
3.2 Color Correction
Underwater imaging is influenced by non-uniformcolorcast
due to absorption of the propagated light. Color cast
corresponds to the varying degrees of light attenuation.
Different wavelengths of light are attenuated at different
rates in water. In our experiments, a simple and effcient
white balance operation is used to enhance the image
appearance by discarding color casts.
The color of a gray image is its distribution of light and dark
pixels. To correct the color, color constancy is applied to an
image to fill the full dynamic range of the image. We can
correct out the gray levels in the center of the range by
applying piece wise linear function according to the
equation.
New pixel = µ* (old pixel - λ1) + λ2
Where new pixel is its result after the transformation.
µ is color corrected gain factor.
λ1 is Average Illumation white balance color pixel.
λ 2 is constant can help to acquire more pleasing results has
been observed.
Fig3: XSG blocks for color correction model.
This white balance method derives the first input of the
fusion process from original underwater image efficiently.It
overcomes the limitations of underwater environments,
removes the color casts and produces a natural appearance
of the underwater image. However, white balance methodis
not sufficient for the improvement of visibility. To obtain a
better enhanced image, the second input of the fusion
process is defined in order to enhance the contrast of the
underwater image.

3.3 Color Enhancement
Despite the color casts caused by light absorption,
underwater images are also affected by low contrast due to
backward scatteringoflight. Contrastenhancementiswidely
utilized in image processing because it brings out more
details of images.
A color intensity transform can also reduce the highlight a
range of pixels i.e white balance pixels while keeping others
pixels further reduce to enrich the colors.
Fig4: color enhancement XSG module
3.4 Multi-scale Fusion Process
In the proposed fusion strategy (shown as Fig.1), color
corrected image and color enhanced image are the first and
second inputs respectively.Byapplyingthe waveletoperator
each input is decomposed into low frequency and high-
frequency components. Then, different fusion principles are
utilized to fuse the low frequency and high frequency
components. The weighted average is enforced to fuse low-
frequency components, while local variance is employed in
the fusion of high frequency components. The new low
frequency and high frequency components are generated,
After the fusion process. Finally, the enhanced image is
obtained by reconstructing the new low frequency and high
frequency components.
3.5Decomposition,FusionandInverseComposition
The wavelet-based fusion algorithm consists of a sequence
of low pass and high pass filter banks that are used to
eliminate unwanted low and high frequencies present inthe
image and to acquire the detail and approximation
coefficients separately for making the fusing process
convenient. one level, 2- dimensional decomposition of the
input image into its detail and approximation coefficients is
described. Each input image is filtered and down-sampled.
The factor of 2 in the algorithm is used to divide the
information contained in the input signal into two equal
parts at each step of filtering so that the information can be
analyzed deeply. There are two steps in level one; the first
step is achieved by applying the low pass and high pass
filters with down-sampling on the rows of the input image
x(r, c). This generates horizontal approximations and
horizontal details respectively. In the next step, the columns
in the horizontal coefficients are filtered and down-sampled
into four sub images Approximate (LL), Vertical detail (LH),
Horizontal detail (HL), and the Diagonal detail (HH).
At the second level of decomposition, the decomposed
approximate image (LL) of the first level becomes the input
image and the process is repeated to scaledowncoefficients.
Each input image is decomposed into its waveletcoefficients
by using the procedure as described above. In our case both
enhanced images: the color corrected and the contrast
enhanced versions of the input image are decomposed into
their wavelet coefficients then both decompositions are
fused by using coefficients of maximum values.
After combining coefficients of both enhanced images into
fused coefficients, the inverse composition is applied to get
the synthesized image. For the inverse composition, the
reverse process is carried out with the help of up-sampling
and filtering steps using filter banks to get a synthesized or
enhanced image y(r,c). Since we are dealing with discrete
data sets
Figure 5: Two-level-2D decomposition, fusion of
coefficients and image synthesizing
so in digital image processing, each input image is
decomposed into its coefficients and inversely composed
into a synthesizedimagebyusingdiscrete wavelettransform
(DWT) and Inverse discrete wavelet transform (IDWT)
respectively. In Fig.5, a completepictureoftwolevel discrete
wavelet-based decompositions, fusion and inverse
composition of enhanced image is shown.
3.6 Image Post-processing
The image post processing blocksareusedtoconvert back to
floating point type as shown in Fig6. It also includes a buffer
block which converts scalar samples to frame output at
lower sampling rate, followed by convert 1-D to 2-D,
transpose blocks.
Fig6: Image Post-Processing

4. Results And Discussion
Here, underwater image enhancement by wavelet
decompositionalgorithmsis implementedonFPGAplatform.
Results for software and hardware implementation is
observed and compared. Resource utilization for algorithm
are observed with the help of design summary overview.
Here MATLAB R2009a and ISE 13.1 softwares are used with
Spartan 3e FPGA board.

a)
b)
fig7:a)input underwater images, b)enhanced underwater
images
Table-1: System performance
Table-2: Resource Utilization
Table-3: Comparative Analysis Table
Table- 1 provides the system performance parameters value
which are calculated for different cases. Table-2 provides
detailed resource utilization and various Timing & Power
parameters for underwater image enhancement. Table-3
details the comparative analysis with the reference values
and the proposed parameters value.
5. CONCLUSIONS
The underwater images quality degraded due to scattering
of light, refraction and absorption parameters. To resolve
these issues and to improve the quality of an underwater
image, a number of techniques are proposed in recentyears.
We have done literature survey on the underwater image
and conclude that the hybridization of algorithms isdonefor
better visualization like wavelet fusion and contrast
enhancement, improving contrast and color correction, etc.
In introduction and literature review, underwater image
enhancement is presented covering basic enhancement
technique, issues and challenges and existing techniques for
underwater image enhancement.Thiswork presentsthe mix
module of implementation of underwater image
enhancement on FPGA based on fusion by wavelet
decomposition. The enhancement method effectively
improves the visibility of underwater images and produces
the lowest MSE and the highest PSNR values. The qualitative
results depict that the proposed method has enhanced the
quality of the hazy underwater images. The quantitative
analysis shows the quality of the image is also maintained.
Performance analysis of proposed method iscompared with
the design provided in the literature. Also, JTAG hardware
co-simulation approach using Xilinx System Generator is
found to be easy and efficient approach for hardware
implementation over FPGA platform.

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BIOGRAPHIES
Venktesh R Kawle received B.E.
degree in Electronics and
Telecommunication from Sipna
COET, Amravati in 2015. He is now
pursuing his M.Tech in Electronics
System and Communication from
GCOE, Amravati, Maharashtra.
A. M. Shah received the B.E.
degree in Electronics and
Telecommunication from SGB
University, Amravati in 2004 and
the M. Tech. degree in Electronics
(VLSI) from RTM University,
Nagpur in 2008. He is working as
Assistant Professor, Electronics
Engineering Department,
Government College of
Engineering Amravati.

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