图像处理之增强---图像增强算法四种，图示与源码，包括retinex（ssr、msr、msrcr）和一种混合算法
申明：本文非笔者原创，原文转载自：http://blog.csdn.net/onezeros/article/details/6342661
两组图像：左边较暗，右边较亮
第一行是原图像，他们下面是用四种算法处理的结果
依次为：
1.一种混合算法
2.msr，multi-scale retinex
3.msrcr，multi-scale retinex with color restoration
4.ssr，single scale retinex
源码，retinex算法的三种，其源码是国外一个研究生的毕设项目
头文件：
/*
* Copyright (c) 2006, Douglas Gray (dgray@soe.ucsc.edu, dr.de3ug@gmail.com)
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the <organization> nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY Douglas Gray ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL <copyright holder> BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#pragma once
#include "cv.h"
extern double* CreateKernel(double sigma);
extern int* CreateFastKernel(double sigma);
extern void FilterGaussian(IplImage* img, double sigma);
extern void FastFilter(IplImage *img, double sigma);
extern void Retinex
(IplImage *img, double sigma, int gain = 128, int offset = 128);
extern void MultiScaleRetinex
(IplImage *img, int scales, double *weights, double *sigmas, int gain = 128, int offset = 128);
extern void MultiScaleRetinexCR
(IplImage *img, int scales, double *weights, double *sigmas, int gain = 128, int offset = 128,
double restoration_factor = 6, double color_gain = 2);
实现：
/*
* Copyright (c) 2006, Douglas Gray (dgray@soe.ucsc.edu, dr.de3ug@gmail.com)
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the <organization> nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY Douglas Gray ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL <copyright holder> BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "retinex.h"
#include <math.h>
//#define USE_EXACT_SIGMA
#define pc(image, x, y, c) image->imageData[(image->widthStep * y) + (image->nChannels * x) + c]
#define INT_PREC 1024.0
#define INT_PREC_BITS 10
inline double int2double(int x) { return (double)x / INT_PREC; }
inline int double2int(double x) { return (int)(x * INT_PREC + 0.5); }
inline int int2smallint(int x) { return (x >> INT_PREC_BITS); }
inline int int2bigint(int x) { return (x << INT_PREC_BITS); }
//
// CreateKernel
//
// Summary:
// Creates a normalized 1 dimensional gaussian kernel.
//
// Arguments:
// sigma - the standard deviation of the gaussian kernel.
//
// Returns:
// double* - an array of values of length ((6*sigma)/2) * 2 + 1.
//
// Note:
// Caller is responsable for deleting the kernel.
//
double*
CreateKernel(double sigma)
{
int i, x, filter_size;
double* filter;
double sum;
// Reject unreasonable demands
if ( sigma > 200 ) sigma = 200;
// get needed filter size (enforce oddness)
filter_size = (int)floor(sigma*6) / 2;
filter_size = filter_size * 2 + 1;
// Allocate kernel space
filter = new double[filter_size];
// Calculate exponential
sum = 0;
for (i = 0; i < filter_size; i++) {
x = i - (filter_size / 2);
filter[i] = exp( -(x*x) / (2*sigma*sigma) );
sum += filter[i];
}
// Normalize
for (i = 0, x; i < filter_size; i++)
filter[i] /= sum;
return filter;
}
//
// CreateFastKernel
//
// Summary:
// Creates a faster gaussian kernal using integers that
// approximate floating point (leftshifted by 8 bits)
//
// Arguments:
// sigma - the standard deviation of the gaussian kernel.
//
// Returns:
// int* - an array of values of length ((6*sigma)/2) * 2 + 1.
//
// Note:
// Caller is responsable for deleting the kernel.
//
int*
CreateFastKernel(double sigma)
{
double* fp_kernel;
int* kernel;
int i, filter_size;
// Reject unreasonable demands
if ( sigma > 200 ) sigma = 200;
// get needed filter size (enforce oddness)
filter_size = (int)floor(sigma*6) / 2;
filter_size = filter_size * 2 + 1;
// Allocate kernel space
kernel = new int[filter_size];
fp_kernel = CreateKernel(sigma);
for (i = 0; i < filter_size; i++)
kernel[i] = double2int(fp_kernel[i]);
delete fp_kernel;
return kernel;
}
//
// FilterGaussian
//
// Summary:
// Performs a gaussian convolution for a value of sigma that is equal
// in both directions.
//
// Arguments:
// img - the image to be filtered in place.
// sigma - the standard deviation of the gaussian kernel to use.
//
void
FilterGaussian(IplImage* img, double sigma)
{
int i, j, k, source, filter_size;
int* kernel;
IplImage* temp;
int v1, v2, v3;
// Reject unreasonable demands
if ( sigma > 200 ) sigma = 200;
// get needed filter size (enforce oddness)
filter_size = (int)floor(sigma*6) / 2;
filter_size = filter_size * 2 + 1;
kernel = CreateFastKernel(sigma);
temp = cvCreateImage(cvSize(img->width, img->height), img->depth, img->nChannels);
// filter x axis
for (j = 0; j < temp->height; j++)
for (i = 0; i < temp->width; i++) {
// inner loop has been unrolled
v1 = v2 = v3 = 0;
for (k = 0; k < filter_size; k++) {
source = i + filter_size / 2 - k;
if (source < 0) source *= -1;
if (source > img->width - 1) source = 2*(img->width - 1) - source;
v1 += kernel[k] * (unsigned char)pc(img, source, j, 0);
if (img->nChannels == 1) continue;
v2 += kernel[k] * (unsigned char)pc(img, source, j, 1);
v3 += kernel[k] * (unsigned char)pc(img, source, j, 2);
}
// set value and move on
pc(temp, i, j, 0) = (char)int2smallint(v1);
if (img->nChannels == 1) continue;
pc(temp, i, j, 1) = (char)int2smallint(v2);
pc(temp, i, j, 2) = (char)int2smallint(v3);
}
// filter y axis
for (j = 0; j < img->height; j++)
for (i = 0; i < img->width; i++) {
v1 = v2 = v3 = 0;
for (k = 0; k < filter_size; k++) {
source = j + filter_size / 2 - k;
if (source < 0) source *= -1;
if (source > temp->height - 1) source = 2*(temp->height - 1) - source;
v1 += kernel[k] * (unsigned char)pc(temp, i, source, 0);
if (img->nChannels == 1) continue;
v2 += kernel[k] * (unsigned char)pc(temp, i, source, 1);
v3 += kernel[k] * (unsigned char)pc(temp, i, source, 2);
}
// set value and move on
pc(img, i, j, 0) = (char)int2smallint(v1);
if (img->nChannels == 1) continue;
pc(img, i, j, 1) = (char)int2smallint(v2);
pc(img, i, j, 2) = (char)int2smallint(v3);
}
cvReleaseImage( &temp );
delete kernel;
}
//
// FastFilter
//
// Summary:
// Performs gaussian convolution of any size sigma very fast by using
// both image pyramids and seperable filters. Recursion is used.
//
// Arguments:
// img - an IplImage to be filtered in place.
//
void
FastFilter(IplImage *img, double sigma)
{
int filter_size;
// Reject unreasonable demands
if ( sigma > 200 ) sigma = 200;
// get needed filter size (enforce oddness)
filter_size = (int)floor(sigma*6) / 2;
filter_size = filter_size * 2 + 1;
// If 3 sigma is less than a pixel, why bother (ie sigma < 2/3)
if(filter_size < 3) return;
// Filter, or downsample and recurse
if (filter_size < 10) {
#ifdef USE_EXACT_SIGMA
FilterGaussian(img, sigma)
#else
cvSmooth( img, img, CV_GAUSSIAN, filter_size, filter_size );
#endif
}
else {
if (img->width < 2 || img->height < 2) return;
IplImage* sub_img = cvCreateImage(cvSize(img->width / 2, img->height / 2), img->depth, img->nChannels);
cvPyrDown( img, sub_img );
FastFilter( sub_img, sigma / 2.0 );
cvResize( sub_img, img, CV_INTER_LINEAR );
cvReleaseImage( &sub_img );
}
}
//
// Retinex
//
// Summary:
// Basic retinex restoration. The image and a filtered image are converted
// to the log domain and subtracted.
//
// Arguments:
// img - an IplImage to be enhanced in place.
// sigma - the standard deviation of the gaussian kernal used to filter.
// gain - the factor by which to scale the image back into visable range.
// offset - an offset similar to the gain.
//
void
Retinex(IplImage *img, double sigma, int gain, int offset)
{
IplImage *A, *fA, *fB, *fC;
// Initialize temp images
fA = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
fB = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
fC = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
// Compute log image
cvConvert( img, fA );
cvLog( fA, fB );
// Compute log of blured image
A = cvCloneImage( img );
FastFilter( A, sigma );
cvConvert( A, fA );
cvLog( fA, fC );
// Compute difference
cvSub( fB, fC, fA );
// Restore
cvConvertScale( fA, img, gain, offset);
// Release temp images
cvReleaseImage( &A );
cvReleaseImage( &fA );
cvReleaseImage( &fB );
cvReleaseImage( &fC );
}
//
// MultiScaleRetinex
//
// Summary:
// Multiscale retinex restoration. The image and a set of filtered images are
// converted to the log domain and subtracted from the original with some set
// of weights. Typicaly called with three equaly weighted scales of fine,
// medium and wide standard deviations.
//
// Arguments:
// img - an IplImage to be enhanced in place.
// sigma - the standard deviation of the gaussian kernal used to filter.
// gain - the factor by which to scale the image back into visable range.
// offset - an offset similar to the gain.
//
void
MultiScaleRetinex(IplImage *img, int scales, double *weights, double *sigmas, int gain, int offset)
{
int i;
double weight;
IplImage *A, *fA, *fB, *fC;
// Initialize temp images
fA = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
fB = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
fC = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
// Compute log image
cvConvert( img, fA );
cvLog( fA, fB );
// Normalize according to given weights
for (i = 0, weight = 0; i < scales; i++)
weight += weights[i];
if (weight != 1.0) cvScale( fB, fB, weight );
// Filter at each scale
for (i = 0; i < scales; i++) {
A = cvCloneImage( img );
FastFilter( A, sigmas[i] );
cvConvert( A, fA );
cvLog( fA, fC );
cvReleaseImage( &A );
// Compute weighted difference
cvScale( fC, fC, weights[i] );
cvSub( fB, fC, fB );
}
// Restore
cvConvertScale( fB, img, gain, offset);
// Release temp images
cvReleaseImage( &fA );
cvReleaseImage( &fB );
cvReleaseImage( &fC );
}
//
// MultiScaleRetinexCR
//
// Summary:
// Multiscale retinex restoration with color restoration. The image and a set of
// filtered images are converted to the log domain and subtracted from the
// original with some set of weights. Typicaly called with three equaly weighted
// scales of fine, medium and wide standard deviations. A color restoration weight
// is then applied to each color channel.
//
// Arguments:
// img - an IplImage to be enhanced in place.
// sigma - the standard deviation of the gaussian kernal used to filter.
// gain - the factor by which to scale the image back into visable range.
// offset - an offset similar to the gain.
// restoration_factor - controls the non-linearaty of the color restoration.
// color_gain - controls the color restoration gain.
//
void
MultiScaleRetinexCR(IplImage *img, int scales, double *weights, double *sigmas,
int gain, int offset, double restoration_factor, double color_gain)
{
int i;
double weight;
IplImage *A, *B, *C, *fA, *fB, *fC, *fsA, *fsB, *fsC, *fsD, *fsE, *fsF;
// Initialize temp images
fA = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
fB = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
fC = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
fsA = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, 1);
fsB = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, 1);
fsC = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, 1);
fsD = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, 1);
fsE = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, 1);
fsF = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, 1);
// Compute log image
cvConvert( img, fB );
cvLog( fB, fA );
// Normalize according to given weights
for (i = 0, weight = 0; i < scales; i++)
weight += weights[i];
if (weight != 1.0) cvScale( fA, fA, weight );
// Filter at each scale
for (i = 0; i < scales; i++) {
A = cvCloneImage( img );
FastFilter( A, sigmas[i] );
cvConvert( A, fB );
cvLog( fB, fC );
cvReleaseImage( &A );
// Compute weighted difference
cvScale( fC, fC, weights[i] );
cvSub( fA, fC, fA );
}
// Color restoration
if (img->nChannels > 1) {
A = cvCreateImage(cvSize(img->width, img->height), img->depth, 1);
B = cvCreateImage(cvSize(img->width, img->height), img->depth, 1);
C = cvCreateImage(cvSize(img->width, img->height), img->depth, 1);
// Divide image into channels, convert and store sum
cvCvtPixToPlane( img, A, B, C, NULL );
cvConvert( A, fsA );
cvConvert( B, fsB );
cvConvert( C, fsC );
cvReleaseImage( &A );
cvReleaseImage( &B );
cvReleaseImage( &C );
// Sum components
cvAdd( fsA, fsB, fsD );
cvAdd( fsD, fsC, fsD );
// Normalize weights
cvDiv( fsA, fsD, fsA, restoration_factor);
cvDiv( fsB, fsD, fsB, restoration_factor);
cvDiv( fsC, fsD, fsC, restoration_factor);
cvConvertScale( fsA, fsA, 1, 1 );
cvConvertScale( fsB, fsB, 1, 1 );
cvConvertScale( fsC, fsC, 1, 1 );
// Log weights
cvLog( fsA, fsA );
cvLog( fsB, fsB );
cvLog( fsC, fsC );
// Divide retinex image, weight accordingly and recombine
cvCvtPixToPlane( fA, fsD, fsE, fsF, NULL );
cvMul( fsD, fsA, fsD, color_gain);
cvMul( fsE, fsB, fsE, color_gain );
cvMul( fsF, fsC, fsF, color_gain );
cvCvtPlaneToPix( fsD, fsE, fsF, NULL, fA );
}
// Restore
cvConvertScale( fA, img, gain, offset);
// Release temp images
cvReleaseImage( &fA );
cvReleaseImage( &fB );
cvReleaseImage( &fC );
cvReleaseImage( &fsA );
cvReleaseImage( &fsB );
cvReleaseImage( &fsC );
cvReleaseImage( &fsD );
cvReleaseImage( &fsE );
cvReleaseImage( &fsF );
}
这种混合算法代码：
我参考一个matlab代码写的
c版
// author : onezeros.lee@gmail.com
// data : 4/20/2011
/*
%%%%%%%%%%%%%%%RGB normalization%%%%%%%%%%%%%%%%%%%%%%
%its cascaded implementation of 1 section of paper "A FAST SKIN REGION DETECTOR" by
%Phil Chen, Dr.Christos Greecos
%and
%section 2.1 of paper "Simple and accurate face detection in color images" by
%YUI TING PAI et al
% Coding by Madhava.S.Bhat, Dept of Electronics an communication
%Dr.Ambedkar Institute of Technology, Bangalore
%madhava.s@dr-ait.org
*/
#include <cv.h>
#include <cxcore.h>
#include <highgui.h>
#include <iostream>
#include <algorithm>
using namespace std;
void cvCompensationGlobal1(IplImage* _src,IplImage* dst)
{
static const int B=0;
static const int G=1;
static const int R=2;
int width=_src->width;
int height=_src->height;
//double
IplImage* src=cvCreateImage(cvGetSize(_src),IPL_DEPTH_64F,3);
CvScalar minV=cvScalar(255,255,255,0);
for (int h=0;h<height;h++) {
unsigned char* p=(unsigned char*)_src->imageData+h*_src->widthStep;
double* pc=(double*)(src->imageData+h*src->widthStep);
for (int w=0;w<width;w++) {
for (int i=0;i<3;i++) {
*pc=*p;
if (minV.val[i]>*pc) {
minV.val[i]=*pc;
}
pc++;
p++;
}
}
}
cvSubS(src,minV,src);
int blackNum=0;
double total=0;
double acc[3]={0};
for (int h=0;h<height;h++) {
double* p=(double*)(src->imageData+h*src->widthStep);
for (int w=0;w<width;w++) {
if (p[0]<0.001&&p[1]<0.001&&p[2]<0.001) {
blackNum++;
p+=3;
continue;
}
double a=p[R];
double b=p[R];
if (p[B]<a) {
a=p[B];
}else {
b=p[B];
}
if (p[G]<a) {
a=p[G];
}else {
b=p[G];
}
total+=a+b;
for (int i=0;i<3;i++) {
acc[i]+=p[i];
}
p+=3;
}
}
double avgT=total/(2*(width*height-blackNum));
CvScalar avg;
IplImage* rgb[3];
for (int i=0;i<3;i++) {
rgb[i]=cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,1);
avg.val[i]=(double)acc[i]/(width*height);
avg.val[i]=avgT/avg.val[i];
}
cvSplit(src,rgb[0],rgb[1],rgb[2],0);
for (int i=0;i<3;i++) {
cvScale(rgb[i],rgb[i],avg.val[i]);//bigger than 255
}
cvMerge(rgb[0],rgb[1],rgb[2],0,src);
//y component only
IplImage* y=cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,1);
for (int h=0;h<height;h++) {
double* psrc=(double*)(src->imageData+h*src->widthStep);
double* py=(double*)(y->imageData+h*y->widthStep);
for (int w=0;w<width;w++) {
*py++=psrc[R]*0.299+psrc[G]*0.587+psrc[B]*0.114;
psrc+=3;
}
}
double maxY=0;
double minY=0;
cvMinMaxLoc(y,&minY,&maxY);
double sumY=0;
//scale
for (int h=0;h<height;h++) {
double* p=(double*)(y->imageData+h*y->widthStep);
for (int w=0;w<width;w++) {
*p=(*p-minY)/(maxY-minY);
sumY+=*p;
p++;
}
}
sumY*=255;
sumY/=width*height;
double scale=1.;
if (sumY<64) {
scale=1.4;
}else if (sumY>192) {
scale=0.6;
}
if (abs(scale-1.)>0.001) {
for (int h=0;h<height;h++) {
double* psrc=(double*)(src->imageData+h*src->widthStep);
unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
double t[3];
for (int w=0;w<width;w++) {
t[0]=pow(psrc[R],scale);
t[1]=pow(psrc[G],scale);
t[2]=psrc[B];
for (int i=0;i<3;i++) {
if (t[i]>255) {
pdst[i]=255;
}else {
pdst[i]=(unsigned char)t[i];
}
}
psrc+=3;
pdst+=3;
}
}
}else{
for (int h=0;h<height;h++) {
double* psrc=(double*)(src->imageData+h*src->widthStep);
unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
for (int w=0;w<width;w++) {
for (int i=0;i<3;i++) {
double t=*psrc++;
if (t>255) {
*pdst++=255;
}else {
*pdst++=(unsigned char)t;
}
}
}
}
}
//free memory
cvReleaseImage(&src);
cvReleaseImage(&y);
for (int i=0;i<3;i++) {
cvReleaseImage(&rgb[i]);
}
}
matlab 版，算法没问题，但代码写的不太好，我做了点修改
%%%%%%%%%%%%%%%RGB normalisation%%%%%%%%%%%%%%%%%%%%%%
%its cascaded implementain of 1 section of paper "A FAST SKIN REGION DETECTOR" by
%Phil Chen, Dr.Christos Greecos
%and
%section 2.1 of paper "Simple and accurate face detection in color images" by
%YUI TING PAI et al
% Coding by Madhava.S.Bhat, Dept of Electronics an communication
%Dr.Ambedkar Institute of Technology, Bangalore
%madhava.s@dr-ait.org
function[]= imag_improve_rgb(IMG)
figure,imshow(IMG)
title('original')
R=double(IMG(:,:,1));
G=double(IMG(:,:,2));
B=double(IMG(:,:,3));
[H,W]=size(R);
% minR=0;
% minG=0;
% minB=0;
% [srow,scol]=find(R==0 & G==0 & B==0);
% if(isempty(srow) && isempty(scol))
minR=min(min(R))
minG=min(min(G))
minB=min(min(B))
% end
R=R-minR;
G=G-minG;
B=B-minB;
S=zeros(H,W);
[srow,scol]=find(R==0 & G==0 & B==0);
[sm,sn]=size(srow);
for i=1:sm
S(srow(i),scol(i))=1;
end
mstd=sum(sum(S))
Nstd=(H*W)-mstd;
Cst=0;
Cst=double(Cst);
for i=1:H
for j=1:W
a=R(i,j);
b=R(i,j);
if(B(i,j)<a)
a=B(i,j);
else
b=B(i,j);
end
if(G(i,j)<a)
a=G(i,j);
else
b=G(i,j);
end
Cst=a+b+Cst;
end
end
%%%%sum of black pixels%%%%%%%%%%%
blacksumR=0;
blacksumG=0;
blacksumB=0;
for i=1:sm
blacksumR=blacksumR+R(srow(i),scol(i));
blacksumG=blacksumG+G(srow(i),scol(i));
blacksumB=blacksumB+B(srow(i),scol(i));
end
Cstd = Cst/(2*Nstd)
CavgR=sum(sum(R))./(H*W)
CavgB=sum(sum(B))./(H*W)
CavgG=sum(sum(G))./(H*W)
Rsc=Cstd./CavgR
Gsc=Cstd./CavgG
Bsc=Cstd/CavgB
R=R.*Rsc;
G=G.*Gsc;
B=B.*Bsc;
C(:,:,1)=R;
C(:,:,2)=G;
C(:,:,3)=B;
C=C/255;
YCbCr=rgb2ycbcr(C);
Y=YCbCr(:,:,1);
figure,imshow(C)
title('aft 1st stage of compensation')
%normalize Y
minY=min(min(Y));
maxY=max(max(Y));
Y=255.0*(Y-minY)./(maxY-minY);
YEye=Y;
Yavg=sum(sum(Y))/(W*H)
T=1;
if (Yavg<64)
T=1.4
elseif (Yavg>192)
T=0.6
end
T
if (T~=1)
RI=R.^T;
GI=G.^T;
else
RI=R;
GI=G;
end
Cfinal(:,:,1)=uint8(RI);
Cfinal(:,:,2)=uint8(GI);
Cfinal(:,:,3)=uint8(B);
figure,imshow(Cfinal)
title('Light intensity compensated')
% YCbCr=rgb2ycbcr(Cnew);
