openCV关于暗原色方法去雾的源程序

#include

#include

#include

char tbarname1[] = "调节block";

//定义两个滑动条,用于调节参数

char tbarname2[] = "调节w";

//w是为了保留一部分的雾

int block=5;

int w1=80;

double w;

IplImage *src=NULL;

IplImage *dst=NULL;



//定义去雾函数如下

IplImage *quw(IplImage *src,int block,double w)

{

//图像分别有三个颜色通道

IplImage *dst1=NULL;

IplImage *dst2=NULL;

IplImage *dst3=NULL;

IplImage *imgroi1;

//dst1的ROI

IplImage *imgroi2;

//dst2的ROI

IplImage *imgroi3;

//dst3的ROI

IplImage *roidark;

//dark channel的ROI

IplImage *dark_channel=NULL;

//暗原色先验的指针

IplImage *toushelv=NULL;

//透射率



//去雾算法运算后的三个通道

IplImage *j1=NULL;

IplImage *j2=NULL;

IplImage *j3=NULL;

//去雾后的图像,三通道合并成

IplImage *dst=NULL;

//源图像ROI位置以及大小

CvRect ROI_rect;



//分离的三个通道

dst1=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

dst2=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

dst3=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);



//为各个ROI分配内存

imgroi1=cvCreateImage(cvSize(block,block),IPL_DEPTH_8U,1);

imgroi2=cvCreateImage(cvSize(block,block),IPL_DEPTH_8U,1);

imgroi3=cvCreateImage(cvSize(block,block),IPL_DEPTH_8U,1);

roidark=cvCreateImage(cvSize(block,block),IPL_DEPTH_8U,1);



//为j1 j2 j3分配大小

j1=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

j2=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

j3=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);



//为暗原色先验指针分配大小

dark_channel=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

//为透射率指针分配大小

toushelv=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

//dst分配大小

dst=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,3);

//将原彩色图像分离成三通道

cvSplit(src,dst1,dst2,dst3,NULL);

//求暗原色

ROI_rect.width=block;

ROI_rect.height=block;

ROI_rect.x=0;

ROI_rect.y=0;





int i;

int j;

double min1=0;

double max1=0;

double min2=0;

double max2=0;

double min3=0;

double max3=0;

double min=0;

CvScalar value;

for(i=0;iwidth/block;i++)

{ for(j=0;jheight/block;j++)

{

//分别计算三个通道内ROI的最小值

cvSetImageROI(dst1,ROI_rect);

cvCopy(dst1,imgroi1,NULL);

cvMinMaxLoc(imgroi1,&min1,&max1,NULL,NULL);

cvSetImageROI(dst2,ROI_rect);

cvCopy(dst2,imgroi2,NULL);

cvMinMaxLoc(imgroi2,&min2,&max2,NULL,NULL);

cvSetImageROI(dst3,ROI_rect);

cvCopy(dst3,imgroi3,NULL);

cvMinMaxLoc(imgroi3,&min3,&max3,NULL,NULL);

//求三个通道内最小值的最小值

if(min1
min=min1;

else

min=min2;

if(min>min3)

min=min3;//min为这个ROI中暗原色

value=cvScalar(min,min,min,min);//min放在value中

//min赋予dark_channel中相应的ROI

cvSetImageROI(dark_channel,ROI_rect);

cvSet(roidark,value,NULL);

cvCopy(roidark,dark_channel,NULL);

//释放各个ROI

cvResetImageROI(dst1);

cvResetImageROI(dst2);

cvResetImageROI(dst3);

cvResetImageROI(dark_channel);

//转入下一个ROI

ROI_rect.x=block*i;

ROI_rect.y=block*j;

}

}

//保存暗原色先验的图像

cvSaveImage("f:/dark_channel_prior.jpg",dark_channel);

//利用得到的暗原色先验dark_channel_prior.jpg求大气光强

double min_dark;

double max_dark;

CvPoint min_loc;

CvPoint max_loc;//max_loc是暗原色先验最亮一小块的原坐标

cvMinMaxLoc(dark_channel,&min_dark,&max_dark,&min_loc,&max_loc,NULL);

cout<
ROI_rect.x=max_loc.x;

ROI_rect.y=max_loc.y;

double A_dst1;//定义大气光成分的估计值

double dst1_min;

double A_dst2;

double dst2_min;

double A_dst3;

double dst3_min;

cvSetImageROI(dst1,ROI_rect);

//按照论文方法求大气光强估计值

cvCopy(dst1,imgroi1,NULL);

cvMinMaxLoc(imgroi1,&dst1_min,&A_dst1,NULL,NULL);

cvSetImageROI(dst2,ROI_rect);

cvCopy(dst2,imgroi2,NULL);

cvMinMaxLoc(imgroi2,&dst2_min,&A_dst2,NULL,NULL);

cvSetImageROI(dst3,ROI_rect);

cvC

opy(dst3,imgroi3,NULL);

cvMinMaxLoc(imgroi3,&dst3_min,&A_dst3,NULL,NULL);

cout<
//求透射率

int k;

int l;

CvScalar m;

CvScalar n;//暗原色先验各元素值



for(k=0;kheight;k++)

{

for(l=0;lwidth;l++)

{

m=cvGet2D(dark_channel,k,l);

n=cvScalar(255-w*m.val[0]);

//w目的是保留一部分的雾,使图像看起来真实些

cvSet2D(toushelv,k,l,n);

}

}

cvSaveImage("f:/toushelv.jpg",toushelv);



//求无雾图像

int p,q;

double tx;

double jj1,jj2,jj3;

CvScalar ix,jx;

for(p=0;pheight;p++)

{

for(q=0;qwidth;q++)

{

tx=cvGetReal2D(toushelv,p,q);

tx=tx/255;

if(tx<0.1)

tx=0.1;

ix=cvGet2D(src,p,q);

jj1=(ix.val[0]-A_dst1)/tx+A_dst1;//根据雾产生模型运算,还原出无雾图像

jj2=(ix.val[1]-A_dst2)/tx+A_dst2;

jj3=(ix.val[2]-A_dst3)/tx+A_dst3;

jx=cvScalar(jj1,jj2,jj3,0.0);

cvSet2D(dst,p,q,jx);

}

}

cvSaveImage("f:/removed_haze.jpg",dst);



//释放指针

cvReleaseImage(&dst1);

cvReleaseImage(&dst2);

cvReleaseImage(&dst3);

cvReleaseImage(&imgroi1);

cvReleaseImage(&imgroi2);

cvReleaseImage(&imgroi3);

cvReleaseImage(&roidark);

cvReleaseImage(&dark_channel);

cvReleaseImage(&toushelv);

cvReleaseImage(&j1);

cvReleaseImage(&j2);

cvReleaseImage(&j3);

return dst;

}



void on_trackbar1(int h)

{

dst=quw(src,block,w);

cvShowImage("目的图像",dst);

// cvWaitKey(0);

}

void on_trackbar2(int h)

{



w=(double)w1/100;

dst=quw(src,block,w);

cvShowImage("目的图像",dst);

// cvWaitKey(0);

}

//主函数如下

void main()

{

//打开图像

src=cvLoadImage("8.jpg",-1);

//创造窗口

cvNamedWindow("有雾图像",CV_WINDOW_AUTOSIZE);

cvShowImage("有雾图像",src);

cvNamedWindow("目的图像",CV_WINDOW_AUTOSIZE);

cvCreateTrackbar(tbarname1, "目的图像", &block, 15, on_trackbar1);

cvCreateTrackbar(tbarname2, "目

的图像", &w1, 100, on_trackbar2);

cvWaitKey(0);

cvReleaseImage(&src);

cvReleaseImage(&dst);

}