图像的处理算法.docx

直方图.if(m_Image.IsNull())return;int h=m_Image.GetHeight();int w=m_Image.GetWidth();int count[256];OnGray();memset(count,0,sizeof(int)*256);//数组初始化for (int i=0;ih;i++)//统计数据{for (int j=0;jw;j++){(count[m_Image.m_pBits[0][i][j]])++;}}double p[256]={0.00}; //统计概率for (int i=0;i256;i++){p[i]=(double)count[i]/a; //*****很重要，先转换，后赋值。}/////////////上面为直方图，加上下面为均衡化///////////////////double s[256]={0.0}; //变换函数for(int i=0;i256;i++){if (i==0)s[i]=p[i];else{s[i]=s[i-1]+p[i]; }}int b=0; //变换灰度等级for (int i=0;ih;i++){for (int j=0;jw;j++){int c=m_Image.m_pBits[0][i][j];b=256*s[c];m_Image.m_pBits[0][i][j]=b;m_Image.m_pBits[1][i][j]=b;m_Image.m_pBits[2][i][j]=b;}}Invalidate(1);直方图规格化int h=m_Image.GetHeight();int w=m_Image.GetWidth();size=w*h;int count[256]={0};//统计灰度值for (int i=0;ih;i++){for (int j=0;jw;j++){(count[m_Image.m_pBits[0][i][j]])++;}}double p0[256]={0.0};//统计每个灰度级概率for (int i=0;i256;i++){p0[i]=(double)count[i]/(size);}double s0[256]={0.0};//均衡概率和for(int i=0;i256;i++){if (i==0){s0[i]=p0[i];} else{s0[i]=s0[i-1]+p0[i];}}double p1[256]={0.0};//规定灰度级概率for(int i=0;i256;i++){p1[i]=(double)(i+128)/(256*256);}double s1[256]={0.0};for(int i=0;i256;i++)//规定灰度级概率和{if (i==0){s1[i]=p1[i];} else{s1[i]=s1[i-1]+p1[i];}}double count0[256]={0.0};for (int i=0;i256;i++)//找最小的规定灰度级与原灰度级对应{double min0=2,minj=0;for (int j=0;j256;j++){if (abs(s0[i]-s1[j])min0){min0=abs(s0[i]-s1[j]);minj=j;}}count0[i]=minj;}for (int i=0;ih;i++){for (int j=0;jw;j++){m_Image.m_pBits[0][i][j]=count0[m_Image.m_pBits[0][i][j]];m_Image.m_pBits[1][i][j]=count0[m_Image.m_pBits[0][i][j]];m_Image.m_pBits[2][i][j]=count0[m_Image.m_pBits[0][i][j]];} Invalidate(1);}自适应均值滤波intmax_template=dlg.m_max_template;intmin_template=dlg.m_min_template;intcopy_min_template=min_template;intval=0;//中间值m_Image.m_nBits= (BYTE***)new BYTE**[3];//开辟三维空间 for (int i=0;i3;i++) {m_Image.m_nBits[i]=(BYTE**)new BYTE*[h]; } for (int i=0;i3;i++) { for (int j=0;jh;j++)