Abstract:
As
light is weakened when spreading in water so the images in the water and their
clarity varies at every point so the
objects in the underwater images are not clearly visible and due to low
contrast and scattering of light and the large noise present in the environment
the images provide no clarity. This paper represent a method called Contrast
Limited Adaptive Histogram Equalization(CLAHE) this method is developed for underwater
images enhancement. This method first apply (CLAHE) on RGB and HSI and then
combine the results using Euclidean normalization. After this we convert it
into RGB and apply (CLAHE) on it and the we convert it into HSV and apply (CLAHE)
on it and after RGB and the again HSI and again convert it into RGB and gets
our results
Introduction:
Digital Image processing is a
processing of image using computer algorithm on digital images. An input can
either be an image or series of images or a video and the output of image
processing may be either image series of images or a video. Images on different
areas got crupted through many ways e.g due to displacement of lens image
become blur or during the process of transmission of image from one device to another
many pixels got corrupted and images filled up with noise so in these cases and
many other like these one , image processing is tool use to minimize these
issues. Digital image processing was first developed in 1960s at the Jet
propulsion lab, Massachusetts institute of technology, Bell
laboratories, University of Maryland and few more institute. There
are many techniques in image processing like image enhancement ( enhances the
image making information more visible ) Arithmetic
operations( adding images or subtraction
or other arithmetic’s operations) and noise filters (removing noise from images
like impulse noise using filters like Gaussian
filters), image analysis(extracting information from image for many purposes )and
restoration( due to many reasons some parts of image got corrupted so in order
to get information we use many techniques of restoration to restore image).Among
many techniques we use image enhancement in our paper to enhance the quality of
underwater images Image. Enhancement is a process of adjusting the image in
order to get suitable results like removing noise or sharpen the image or
brighten the image making it easy to understand. In enhancement there is a also
many techniques for different kind of images as there can’t be only one method
to enhance every kind of images. For underwater images there is a method called (CLAHE). This technique is used to increases
the contrast of image and differ from original histogram equalizer, [4]as in
histogram equalizer the range and the contrast of the image is modified by
changing its histogram. By using cumulative function as mapping function we can
achieve this. The peaks and the tough of the images are changed and equalized
though out the image using histogram equalization, where as in the Contrast Limited Adaptive Histogram
Equalization(CLAHE) histogram is divided
into many parts using some threshold and
then equalization is applied on it.It is an adaptive contrast histogram
equalization so it adapts
its self-according to the different situations and it applyclahe
on little parts of image called tiles ,the contrast of the image is enhanced
and the resulting neighbor tiles are then stitched back seamlessly using
bili-near interpolation. The contrast in the homogeneous region can be limited
so that noise amplification can be avoided. it is suitable
for improving local contrast and enhancement.
Literature
review:
- Muhammad Suzuri Hitam,Wan Nural
Jawahir Hj Wan Yussof and Ezmahamrul
Afreen Awalludin, Zainuddin Bachok proposed a paper named Mixture Contrast Limited Adaptive Histogram Equalization for
Underwater Image Enhancement . They implemented (CLAHE ) on HSV and save the results and after saving it they converted
them into RGB and apply CLAHE on it and get another results and then they first
normalized both results, by applying HSV and other one by applying CLAHE on RGB
and then combined them using Euclidean
normalization. but their results were not so clear and many details in their
result were lost causing a little blurriness and less contrast image.
- Haocheng Wen and Yonghong Tian+, Tiejun Huang,
Wen Goa also presented “Single
Underwater Image Enhancement with a New Optical Model” in this paper the
use convert their image into RGB and then apply underwater dark channel and
using this they get the distance from the image and also knows when and where
should be the enhancement of image is needed to be done.
- Rajesh kumar Rai1, Puran Gour2, Balvant Singh represents Underwater Image Segmentation using CLAHE
Enhancement and Thresholding.****
Methodology:
No enhancement technique is good enough to be able to apply
on every kind of image. Therefore various techniques are developed to handle
various kind of images and the method discussed in this paper is focusing on
underwater images. (CLAHE) one of the method which are used for under water images enhancement. This paper
first convert image into HSI
The input image is
first convert into HSI where H
represents Hue and S represents Saturation and I represents Intensity .The
intensity component ranges between 0 and 1 in which 0 means black and 1 means white.
The separation of
RGB determines the value of saturation as it is defining the spreading of
color. if the RGB values are closer than the color will be close to grey and if
they are far apart then the color will be quite intense. The range of
saturation so from 0 to 1The Equation to do this is
\(S=\frac{V-\min\left(R,G,B\right)}{V}\)
While the hue is bit
different from other. It defines the whether the color is red blue or
green etc. at 0 degree the color is red
while at 120 degree the color is green .
In order to calculate Hue we must calculate R`, G`, B`
\(R`=\frac{V-R}{V-\min\left(R,G,B\right)}\)
\(PSNR\ =\ 20\log\ \left(\frac{2^{\beta}-1}{\sqrt{\left(MSE\right)}}\right)\)
\(G`=\frac{V-G}{V-\min\left(R,G,B\right)}\)
\(PSNR\ =\ 20\log\ \left(\frac{2^{\beta}-1}{\sqrt{\left(MSE\right)}}\right)\)\(B`=\frac{V-B}{V-\min\left(R,G,B\right)}\)\(B`=\frac{V-B}{V-\min\left(R,G,B\right)}\)
If S=0
then hue is undefined:
H=
\(5+B`\ \ \ \ \ R=\max\left(R,G,B\right)\ and\ G=\min\left(R,G,B\right)\)
\(1-G`\ \ \ \ \ R=\max\left(R,G,B\right)\ and\ G\ !=\min\left(R,G,B\right)\)
\(1-R`\ \ \ \ \ G=\max\left(R,G,B\right)\ and\ B=\min\left(R,G,B\right)\)
\(3-B`\ \ \ \ \ G=\max\left(R,G,B\right)\ and\ B\ !=\min\left(R,G,B\right)\)
\(3+G`\ \ \ \ \ B=\max\left(R,G,B\right)\ \)
\(5-R`\ \ \ \ \ otherwise\) \(5-R`\ \ \ \ \ otherwise\)
As at 360 there is discontinuity in hue and
its quite difficult to perform arithmetic operations so we implement CLAHE on
only Value and Saturation.
RGB define color in form of three colors Green blue and red and this methods combines these three basic
colors and create other colors. Light is added to create form from out of darkness. RGB is the sum of following functions:
\(PSNR\ =\ 20\log\ \left(\frac{2^{\beta}-1}{\sqrt{\left(MSE\right)}}\right)\)\(R=\int_{300}^{830}S\left(\gamma\right)\ R\left(\gamma\right)\ d\gamma\)
\(G=\int_{300}^{830}S\left(\gamma\right)\ G\left(\gamma\right)\ d\gamma\)
\(\)\(B=\int_{300}^{830}S\left(\gamma\right)\ B\left(\gamma\right)\ d\gamma\)
Where S(r) is light spectrum and R(r), G(r), B(r) are the functions for the R, G and B respectively. This paper implements clahe on all three
components individually and then combine the three result to get future
results.
The results obtained from both HSI
and RGB provides undefined artifacts as well as much brightness so we introduce
the combination of both of the results using Euclidean normalization.
The reason to performing this is to increase the contrast of the image.This method normalizes
the results obtained after applying CLAHE on RGB by using this formula:
\(\left[Rc1\ \ Gc1\ \ Bc1\right]\ =\left[\frac{Rc}{Rc+Gc+Bc},\frac{Gc}{Rc+Gc+Bc},\frac{Bc}{Rc+Gc+Bc}\right]\) (1)
(Figure of clahe
implement on RGB):
Whereas the result
of HSI when clahe was implemented on it can be convert into RGB by finding
chorma
\(C=S\ *\ V\)
AND
\(H`=\frac{H}{60^{\theta}}\)
By using both c and
H` we can find
\(X=C(1-|(H`\ mod\ 2)-1|)\)
Conversion of HSI to
RGB can be done using this formula:
( Rc2 Gc2 Bc2 )=
\(\left(0,0,0\right)\ if\ H\ is\ \ undefined\)
\(\left(C,X,0\right)\ if\ 0\le H`\ <1\)
\(\left(X,C,0\right)\ if\ 1\le H`<2\)
\(\left(0,C,X\right)\ if\ 2\le H`<3\)
\(\left(0,X,C\right)\ if\ 3\le H`<4\)
\(\left(X,0,C\right)\ if\ 4\le H`<5\)
\(\left(C,0,X\right)\ if\ 5\le H`<6\) (2)
Finally by getting
results from both eq 1 and eq 2 we can compute
Euclidean normal
form:
\(RGB=\left[\sqrt{R^2c1\ +R^2c2},\sqrt{G^2c1\ +G^2c2},\sqrt{B^2c1\ +B^2c2},\right]\)
(picture of clahe on
his and rgb and after normalization)
After this we covert
the image into HSV where H represents Hue and S represents Saturation and E
represents Value The models was represented by Smith in 1978.In HSV when value
is either max or min then saturation and Hue doesn’t make any difference. By taking the highest value of RGB the value
of V can be calculated as HSV model Takes RGB in rages of 0 to 1. The
computation of RGB can be described as:
\(V=\max(R,G,B)\)
The separation of
RGB determines the value of saturation as it is defining the spreading of color.
if the RGB values are closer than the color will be close to grey and if they
are far apart then the color will be quite intense. The Equation to do this is
\(\)\(S=\frac{V-\min\left(R,G,B\right)}{V}\)
While the hue is bit
different from other. It defines the whether the color is red blue or
green etc. at 0 degree the color is red
while at 120 degree the color is green .
In order to calculate HUE we must calculate R`, G`, B`
\(R`=\frac{V-R}{V-\min\left(R,G,B\right)}\)
\(PSNR\ =\ 20\log\ \left(\frac{2^{\beta}-1}{\sqrt{\left(MSE\right)}}\right)\)
\(G`=\frac{V-G}{V-\min\left(R,G,B\right)}\)
\(PSNR\ =\ 20\log\ \left(\frac{2^{\beta}-1}{\sqrt{\left(MSE\right)}}\right)\)\(B`=\frac{V-B}{V-\min\left(R,G,B\right)}\)\(B`=\frac{V-B}{V-\min\left(R,G,B\right)}\)
If S=0
then hue is undefined:
H=
\(\ \ \ \ \ \ \ \ \ 5+B`\ \ \ \ \ R=\max\left(R,G,B\right)\ and\ G=\min\left(R,G,B\right)\)
\(1-G`\ \ \ \ \ R=\max\left(R,G,B\right)\ and\ G\ !=\min\left(R,G,B\right)\)
\(1-R`\ \ \ \ \ G=\max\left(R,G,B\right)\ and\ B=\min\left(R,G,B\right)\)
\(3-B`\ \ \ \ \ G=\max\left(R,G,B\right)\ and\ B\ !=\min\left(R,G,B\right)\)
\(3+G`\ \ \ \ \ B=\max\left(R,G,B\right)\ \)
\(5-R`\ otherwise\)
As at 360 there is discontinuity in hue and
its quite difficult to perform arithmetic operations so we implement CLAHE on
only Value and Saturation.
After getting the results it was converted
into RGB then HSI and again apply CLAHE on it and finally converted the results
into RGB
(picture of our result and picture of previous
paper)
The
Mean square error\(MSE=\left[\frac{\Sigma\ M,N\ \left[I1\left(m,n\right)\ -\ I2\left(m,n\right)\right]^2}{M\cdot N}\right]\)
The Mean square error (represents cumulative
squared error and peak signal noise ratio(peak error) are the two scales which
re use to compare the quality of enhanced underwater images.
Higher value of both methods represents bad
method and low value represents good method.
MSE can be calculated through:
\(MSE=\left[\frac{\Sigma\ M,N\ \left[I1\left(m,n\right)\ -\ I2\left(m,n\right)\right]^2}{M\cdot N}\right]\)\(MSE=\left[\frac{\Sigma\ M,N\ \left[I1\left(m,n\right)\ -\ I2\left(m,n\right)\right]^2}{M\cdot N}\right]\)
\(MSE=\left[\frac{\Sigma\ M,N\ \left[I1\left(m,n\right)\ -\ I2\left(m,n\right)\right]^2}{M\cdot N}\right]\)
Where l1 and I2 represents the original images
and new image and he size must b same and denoted by M*N whereas PSE is
represented by:
\(PSNR\ =\ 20\log\ \left(\frac{2^{\beta}-1}{\sqrt{\left(MSE\right)}}\right)\)
Where B represents bits per sample
Analysis:
When
this method applied on the images it increases the contouring.
References:
[1] Muhammad Suzuri
Hitam,Wan Nural Jawahir Hj Wan Yussof and Ezmahamrul
Afreen Awalludin, Zainuddin Bachok proposed a paper named Mixture Contrast Limited Adaptive Histogram Equalization for
Underwater Image Enhancement .
[2] Haocheng Wen and Yonghong Tian+, Tiejun Huang,
Wen Gao also presented “Single
Underwater Image Enhancement with a New Optical Model”
[3] [Rajesh
kumar Rai1, Puran Gour2, Balvant Singh represents Underwater Image Segmentation using CLAHE Enhancement and
Thresholding.****
[4] Neethu M.
Sasi, V. K. Jayasree proposed Contrast Limited Adaptive Histogram Equalization for Qualitative
Enhancement of Myocardial Perfusion Images