Abstract:
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 apply (CLAHE) on three color models. Experiments shows that the proposed method reduce noise and improves the visual quality of image through enhancement of contrast.
Introduction:
[1]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. Due to displacement of lens or during the
process of transferring of image from one device to another device many pixels of
image lost during transferring or images filled up with noise so in these cases
and many other like these one, image processing is a tool use to minimize these
issues. [2]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 lost there original shape
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. [3] So for underwater images there
are many methods these methods provide a way to identify the objects in
underwater images. As in underwater images there are poor visibility conditions
like “absorption of light”, “reflection of light”, “scattering of light”,
“bending of light” etc and these conditions are the reason of degradation of
light. The technique used in this paper to enhanced the underwater images is Contrast
Limited Adaptive Histogram Equalizer
(CLAHE). [4]This technique is used to increases
the contrast of image and differ from original histogram equalizer, 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 apply clahe 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 re-gion can be limited
so that noise amplification can be avoided. it is suitable for improving local contrast
and enhancement.
This
paper prosed a method in which CLAHE is implemented on RGB and HSV and HSI
models and then combines them. The goal is to reduce noise using CLAHE.
Literature
review:
[5] They implemented (CLAHE 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.
[6] 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.
[3] used three techniques for the
enhancement of underwater images and then compared those techniques that which
one is best. The techniques used are “contrast stretching” and “Histogram
Equalization” and “Contrast Limited Histogram Equalization” .Contrast
stretching is a method to make brighter portion brighter and dark portion
darker .
[4] In this paper CLAHE is applied on chrominance channels of cardiac nuclear image, by keeping luminance channel unaffected which results in an enhanced image. The experiment carried out through this sequence :
first an input image is given and the image is read into RGB color model and after this next step is to convert the input image into YCbCr color space and then separate the chrominance channels from luminance channels and after this the paper set the number of regions and clip limit for both chrominance channels and then apply channels for both processes separately then interpolate to assembles the final chrominance image then combine the luminance channel with processed chrominance channels and in the end convert it into RGB space.
[7] This paper is proposing a method for the segmentation of underwater image using CLAHE, after applying CLAHE histogram thresholding is applied to segment the object. It also compare various histogram thresh holding techniques and compare them on basis of there performance mean square error
[8] this paper describes that in xray image the images need enhancement so the enhancement technique used by this paper is CLAHE which is suitable for VLSI or FPGA implementation. The main objective of this realization is to minimize latency without losing precision.
[9] This paper proposed a method CLAHE for enhancement of retinal images and by applying CLAHE on Green channel to improve the color retinal image quality
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 1 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`
\(R`=\frac{V-R}{V-\min\left(R,G,B\right)}\)
\(G`=\frac{V-G}{V-\min\left(R,G,B\right)}\)
\(B`=\frac{V-B}{V-\min\left(R,G,B\right)}\)
If S=0
then hue is undefined:
H=
5+B` R=max(R,G,B) and G=min(R,G,B)
1-G` R=max(R,G,B) and G!=min(R,G,B)
1-R` G=max(R,G,B) and B=min(R,G,B)
3-B` G=max(R,G,B) and B!=min(R,G,B)
3+G` B=max(R,G,B)
5-R` otherwie
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:
\(R=\int_{300}^{830}S\left(\gamma\right)\ R\left(\gamma\right)\ d\gamma\)
\(G=\int_{300}^{830}S\left(\gamma\right)\ G\left(\gamma\right)\ d\gamma\)
\(B=\int_{300}^{830}S\left(\gamma\right)\ B\left(\gamma\right)\ d\gamma\)
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`
\(R`=\frac{V-R}{V-\min\left(R,G,B\right)}\)
\(G`=\frac{V-G}{V-\min\left(R,G,B\right)}\)
\(B`=\frac{V-B}{V-\min\left(R,G,B\right)}\)
if S=0 the Hue is:
H=
5+B` R=max(R,G,B) and G=min(R,G,B)
1-G` R=max(R,G,B) and G!=min(R,G,B)
1-R` G=max(R,G,B) and B=min(R,G,B)
3-B` G=max(R,G,B) and B!=min(R,G,B)
3+G` B=max(R,G,B)
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:
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:
\(MSE=\left[\frac{\Sigma\ M,N\ \left[I1\left(m,n\right)\ -\ I2\left(m,n\right)\right]^2}{M\cdot N}\right]\)
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:
[3] Balvant Singh , Ravi
Shankar Mishra , Puran Gour proposed Analysis
of Contrast Enhancement Techniques for Underwater Image
[4] Neethu M. Sasi, V. K. Jayasree proposed
Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of
Myocardial Perfusion Images
[5]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 .
[6] Haocheng
Wen and Yonghong Tian+, Tiejun Huang, Wen Gao also presented “Single Underwater Image Enhancement with a
New Optical Model”
[7] [Rajesh kumar Rai1, Puran Gour2,
Balvant Singh represents Underwater
Image Segmentation using CLAHE Enhancement and Thresholding.
[8] Ali M. Reza presented "Equalization (CLAHE) for Real-Time Image Enhancement".
[9]Agung W.Setiawan, Tati R. Mengko, Oerip S. Santoso poposed "Color retinal image enhancement using CLAHE".