C#简单数字图像处理程序

c#编写的简单数字图像处理程序，数字图像处理的平时成绩和编程作业竟然占50%，那就把最近做的事写个札记吧。

先放个最终做成提交的效果看看：

1.直方图均衡化

2.算子锐化

3.空域增强

一、要达到的目的和效果

  1.打开，保存图片；

  2.获取图像灰度值，图像坐标；

  3.进行线性变换，直方图均衡化处理；

  4.直方图变换增强，以及各种滤波处理；

  5.图像锐化（kirsch,laplace,sobel等算子）。

二、编程环境及语言

c#-windowsform-vs2015

三、图标

最近发现了一个完全免费的矢量图标网站阿里妈妈iconfont，超级好用。

当然也可以自己动手画一个

四、创建窗体

  1.先建一个c#windows窗体应用程序，设置好保存路径和项目名称；

  2.打开工具箱，找到menuscript，加到窗体中，依次填写菜单以及子菜单的名称，菜单里将完成主要的图像处理操作；

  3.因为要显示处理前后的图片，所以再添加两个picturebox控件，可以设置停靠模式为stretchimage；再加两个groupbox，每个groupbox里添加label和textbox控件，用来显示图像灰度值及坐标，这样窗体基本搭建完成，还是挺简单的。

五、主要代码

using system;
using system.collections.generic;
using system.componentmodel;
using system.data;
using system.drawing;
using system.drawing.imaging;
using system.linq;
using system.text;
using system.windows.forms;


namespace text1
{
 public partial class imageenhancement : form
 {
 public imageenhancement()
 {
  initializecomponent();
 }
 bitmap bitmap;
 int iw, ih;
 //打开文件
 private void 打开toolstripmenuitem_click(object sender, eventargs e)
 {
  picturebox1.image = null;//先设置两个picturebox为空
  picturebox2.image = null;
  //使用 openfiledialog类打开图片
  openfiledialog open = new openfiledialog();
  open.filter = "图像文件(*.bmp;*.jpg;*gif;*png;*.tif;*.wmf)|"
   + "*.bmp;*jpg;*gif;*png;*.tif;*.wmf";
  if (open.showdialog() == dialogresult.ok)
  {
  try
  {
   bitmap = (bitmap)image.fromfile(open.filename);
  }
  catch (exception exp) { messagebox.show(exp.message); }
  picturebox1.refresh();
  picturebox1.image = bitmap;
  label6.text = "原图";
  iw = bitmap.width;
  ih = bitmap.height;



  }
 }
 //保存文件
 private void 保存toolstripmenuitem_click(object sender, eventargs e)
 {
  string str;
  savefiledialog savefiledialog1 = new savefiledialog();
  savefiledialog1.filter = "图像文件(*.bmp)|*.bmp|all file(*.*)|*.*";
  savefiledialog1.showdialog();
  str = savefiledialog1.filename;
  picturebox2.image.save(str);


 }
 //退出
 private void 退出toolstripmenuitem_click(object sender, eventargs e)
 {
  this.close();
 }
 private void label5_click(object sender, eventargs e)
 {
 }
 //读取灰度值及坐标
 private void picturebox1_mousedown(object sender, mouseeventargs e)
 {
  color pointrgb = bitmap.getpixel(e.x, e.y);
  textbox1.text = pointrgb.r.tostring();
  textbox2.text = pointrgb.g.tostring();
  textbox3.text = pointrgb.b.tostring();
  textbox4.text = e.x.tostring();
  textbox5.text = e.y.tostring();
  int a = int.parse(textbox1.text);
 }
 //线性变换部分
 private void linearpo_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  linearpoform linearform = new linearpoform();
  if (linearform.showdialog() == dialogresult.ok)
  {
   rectangle rect = new rectangle(0, 0, bitmap.width, bitmap.height);
   system.drawing.imaging.bitmapdata bmpdata = bitmap.lockbits(rect,
   system.drawing.imaging.imagelockmode.readwrite,
   bitmap.pixelformat);
   intptr ptr = bmpdata.scan0;
   //int bytes = bitmap.width *;
  }
  }
 }
 private void textbox4_textchanged(object sender, eventargs e)
 {
 }
 private void label3_click(object sender, eventargs e)
 {
 }
 //对比度扩展
 private void 对比度扩展toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  strechdialog dialog = new strechdialog();


  if (dialog.showdialog() == dialogresult.ok)
  {
   this.text = " 图像增强 对比度扩展 ";
   bitmap bm = new bitmap(picturebox1.image);


   int x1 = convert.toint32(dialog.getx01);
   int y1 = convert.toint32(dialog.gety01);
   int x2 = convert.toint32(dialog.getx02);
   int y2 = convert.toint32(dialog.gety02);


   //计算灰度映射表
   int[] pixmap = pixelsmap(x1, y1, x2, y2);


   //线性拉伸
   bm = stretch(bm, pixmap, iw, ih);


   picturebox2.refresh();
   picturebox2.image = bm;
   label7.text = "对比度扩展结果";
  }
  }
 }

 //计算灰度映射表
 public int[] pixelsmap(int x1, int y1, int x2, int y2)
 {
  int[] pmap = new int[256];  //映射表
  if (x1 > 0)
  {
  double k1 = y1 / x1;  //第1段斜率k1
  //按第1段斜率k1线性变换
  for (int i = 0; i <= x1; i++)
   pmap[i] = (int)(k1 * i);
  }
  double k2 = (y2 - y1) / (x2 - x1); //第2段斜率k2

  //按第2段斜率k2线性变换
  for (int i = x1 + 1; i <= x2; i++)
  if (x2 != x1)
   pmap[i] = y1 + (int)(k2 * (i - x1));
  else
   pmap[i] = y1;

  if (x2 < 255)
  {
  double k3 = (255 - y2) / (255 - x2);//第2段斜率k2

  //按第3段斜率k3线性变换
  for (int i = x2 + 1; i < 256; i++)
   pmap[i] = y2 + (int)(k3 * (i - x2));
  }
  return pmap;
 }

 //对比度扩展函数
 public bitmap stretch(bitmap bm, int[] map, int iw, int ih)
 {
  color c = new color();
  int r, g, b;
  for (int j = 0; j < ih; j++)
  {
  for (int i = 0; i < iw; i++)
  {
   c = bm.getpixel(i, j);
   r = map[c.r];
   g = map[c.g];
   b = map[c.b];
   if (r >= 255) r = 255;
   if (r < 0) r = 0;
   if (g >= 255) g = 255;
   if (g < 0) g = 0;
   if (b >= 255) b = 255;
   if (b < 0) b = 0;
   bm.setpixel(i, j, color.fromargb(r, g, b));
  }
  }
  return bm;
 }
 private void 直方图均衡化toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  this.text = " 图像增强 直方图均衡化";
  bitmap bm = new bitmap(picturebox1.image);
  //获取直方图
  int[] hist = gethist(bm, iw, ih);

  //直方图均匀化
  bm = histequal(bm, hist, iw, ih);

  picturebox2.refresh();
  picturebox2.image = bm;
  label7.text = "直方图均衡化结果";
  flag = true;
  }
 }
 bool flag = false;   //直方图均衡化标志

 //显示直方图
 private void 显示直方图toolstripmenuitem_click(object sender, eventargs e)
 {
  if (flag)
  {
  bitmap b1 = new bitmap(picturebox1.image);
  bitmap b2 = new bitmap(picturebox2.image);

  int[] hist1 = gethist(b1, iw, ih);
  int[] hist2 = gethist(b2, iw, ih);
  drawhist(hist1, hist2);
  }
 }

 //获取直方图
 public int[] gethist(bitmap bm, int iw, int ih)
 {
  int[] h = new int[256];
  for (int j = 0; j < ih; j++)
  {
  for (int i = 0; i < iw; i++)
  {
   int grey = (bm.getpixel(i, j)).r;
   h[grey]++;
  }
  }
  return h;
 }
 //直方图均衡化
 public bitmap histequal(bitmap bm, int[] hist, int iw, int ih)
 {
  color c = new color();
  double p = (double)255 / (iw * ih);
  double[] sum = new double[256];
  int[] outg = new int[256];
  int r, g, b;
  sum[0] = hist[0];
  for (int i = 1; i < 256; i++)
  sum[i] = sum[i - 1] + hist[i];



  //灰度变换:i-->outg[i]
  for (int i = 0; i < 256; i++)
  outg[i] = (int)(p * sum[i]);

  for (int j = 0; j < ih; j++)
  {
  for (int i = 0; i < iw; i++)
  {
   r = (bm.getpixel(i, j)).r;
   g = (bm.getpixel(i, j)).g;
   b = (bm.getpixel(i, j)).b;
   c = color.fromargb(outg[r], outg[g], outg[b]);
   bm.setpixel(i, j, c);
  }
  }
  return bm;
 }

 public void drawhist(int[] h1, int[] h2)
 {
  //画原图直方图------------------------------------------
  graphics g = picturebox1.creategraphics();
  pen pen1 = new pen(color.blue);
  g.clear(this.backcolor);

  //找出最大的数,进行标准化.
  int maxn = h1[0];
  for (int i = 1; i < 256; i++)
  if (maxn < h1[i])
   maxn = h1[i];

  for (int i = 0; i < 256; i++)
  h1[i] = h1[i] * 250 / maxn;

  g.fillrectangle(new solidbrush(color.white), 0, 0, 255, 255);

  pen1.color = color.red;
  for (int i = 0; i < 256; i++)
  g.drawline(pen1, i, 255, i, 255 - h1[i]);

  g.drawstring("" + maxn, this.font, new solidbrush(color.blue), 0, 0);

  label6.text = "原图直方图";

  //画均衡化后直方图------------------------------------------
  g = picturebox2.creategraphics();
  pen1 = new pen(color.blue);
  g.clear(this.backcolor);

  //找出最大的数,进行标准化.
  maxn = h2[0];
  for (int i = 1; i < 256; i++)
  if (maxn < h2[i])
   maxn = h2[i];

  for (int i = 0; i < 256; i++)
  h2[i] = h2[i] * 250 / maxn;

  g.fillrectangle(new solidbrush(color.white), 0, 0, 255, 255);

  pen1.color = color.red;
  for (int i = 0; i < 256; i++)
  g.drawline(pen1, i, 255, i, 255 - h2[i]);

  g.drawstring("" + maxn, this.font, new solidbrush(color.blue), 0, 0);
  label7.text = "均衡化后直方图";
  flag = false;
 }

 private void 阈值滤波toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  this.text = "图像增强 阈值滤波";
  bitmap bm = new bitmap(picturebox1.image);
  //阈值滤波
  bm = threshold(bm, iw, ih);

  picturebox2.refresh();
  picturebox2.image = bm;
  label7.text = "阈值滤波结果";
  }
 }


 //3×3阈值滤波
 public bitmap threshold(bitmap bm, int iw, int ih)
 {
  bitmap obm = new bitmap(picturebox1.image);


  int avr,  //灰度平均
  sum,  //灰度和
  num = 0, //计数器
  nt = 4, //计数器阈值
  t = 50; //阈值
  int pij, pkl, //(i,j),(i+k,j+l)处灰度值
  err;  //误差


  for (int j = 1; j < ih - 1; j++)
  {
  for (int i = 1; i < iw - 1; i++)
  {
   //取3×3块的9个象素, 求和
   sum = 0;
   for (int k = -1; k < 2; k++)
   {
   for (int l = -1; l < 2; l++)
   {
    if ((k != 0) || (l != 0))
    {
    pkl = (bm.getpixel(i + k, j + l)).r;
    pij = (bm.getpixel(i, j)).r;
    err = math.abs(pkl - pij);
    sum = sum + pkl;
    if (err > t) num++;
    }
   }
   }
   avr = (int)(sum / 8.0f);  //平均值
   if (num > nt)
   obm.setpixel(i, j, color.fromargb(avr, avr, avr));
  }
  }
  return obm;
 }

 private void 均值滤波toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  this.text = "数字图像处理";
  bitmap bm = new bitmap(picturebox1.image);
  bm = average(bm, iw, ih);
  picturebox2.refresh();
  picturebox2.image = bm;
  label7.text = "均值滤波结果";
  }
 }
 //均值滤波
 public bitmap average(bitmap bm, int iw, int ih)
 {
  bitmap obm = new bitmap(picturebox1.image);
  for (int j = 1; j < ih - 1; j++)
  {
  for (int i = 1; i < iw - 1; i++)
  {
   int avr;
   int avr1;
   int avr2;
   int sum = 0;
   int sum1 = 0;
   int sum2 = 0;
   for (int k = -1; k <= 1; k++)
   {
   for (int l = -1; l <= 1; l++)
   {
    sum = sum + (bm.getpixel(i + k, j + 1).r);
    sum1 = sum1 + (bm.getpixel(i + k, j + 1).g);
    sum2 = sum2 + (bm.getpixel(i + k, j + 1).b);
   }
   }
   avr = (int)(sum / 9.0f);
   avr1 = (int)(sum1 / 9.0f);
   avr2 = (int)(sum2 / 9.0f);
   obm.setpixel(i, j, color.fromargb(avr, avr1, avr2));
  }
  }
  return obm;
 }

 private void 中值滤波toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {

   this.text = "图像增强 中值滤波";
   bitmap bm = new bitmap(picturebox1.image);
   int num =3;
   //中值滤波
   bm = median(bm, iw, ih, num);

   picturebox2.refresh();
   picturebox2.image = bm;
   label2.location = new point(370, 280);
   if (num == 1) label7.text = "1x5窗口滤波结果";
   else if (num == 2) label7.text = "5x1窗口滤波结果";
   else if (num == 3) label7.text = "5x5窗口滤波结果";

  }
 }

 //中值滤波方法
 public bitmap median(bitmap bm, int iw, int ih, int n)
 {
  bitmap obm = new bitmap(picturebox1.image);
  for (int j = 2; j < ih - 2; j++)
  {
  int[] dt;
  int[] dt1;
  int[] dt2;
  for (int i = 2; i < iw - 2; i++)
  {
   int m = 0, r = 0, r1 = 0, r2 = 0, a = 0, b = 0;
   if (n == 3)
   {
   dt = new int[25];
   dt1 = new int[25];
   dt2 = new int[25];
   //取5×5块的25个象素
   for (int k = -2; k < 3; k++)
   {
    for (int l = -2; l < 3; l++)
    {
    //取(i+k,j+l)处的象素，赋于数组dt
    dt[m] = (bm.getpixel(i + k, j + l)).r;
    dt1[a] = (bm.getpixel(i + k, j + l)).g;
    dt2[b] = (bm.getpixel(i + k, j + l)).b;
    m++;
    a++;
    b++;
    }
   }
   //冒泡排序,输出中值
   r = median_sorter(dt, 25); //中值
   r1 = median_sorter(dt1, 25);
   r2 = median_sorter(dt2, 25);
   }
   else if (n == 1)
   {
   dt = new int[5];

   //取1×5窗口5个像素
   dt[0] = (bm.getpixel(i, j - 2)).r;
   dt[1] = (bm.getpixel(i, j - 1)).r;
   dt[2] = (bm.getpixel(i, j)).r;
   dt[3] = (bm.getpixel(i, j + 1)).r;
   dt[4] = (bm.getpixel(i, j + 2)).r;
   r = median_sorter(dt, 5); //中值
   dt1 = new int[5];


   //取1×5窗口5个像素
   dt1[0] = (bm.getpixel(i, j - 2)).g;
   dt1[1] = (bm.getpixel(i, j - 1)).g;
   dt1[2] = (bm.getpixel(i, j)).g;
   dt1[3] = (bm.getpixel(i, j + 1)).g;
   dt1[4] = (bm.getpixel(i, j + 2)).g;
   r1 = median_sorter(dt1, 5); //中值
   dt2 = new int[5];


   //取1×5窗口5个像素
   dt2[0] = (bm.getpixel(i, j - 2)).b;
   dt2[1] = (bm.getpixel(i, j - 1)).b;
   dt2[2] = (bm.getpixel(i, j)).b;
   dt2[3] = (bm.getpixel(i, j + 1)).b;
   dt2[4] = (bm.getpixel(i, j + 2)).b;
   r2 = median_sorter(dt2, 5); //中值
   }
   else if (n == 2)
   {
   dt = new int[5];


   //取5×1窗口5个像素
   dt[0] = (bm.getpixel(i - 2, j)).r;
   dt[1] = (bm.getpixel(i - 1, j)).r;
   dt[2] = (bm.getpixel(i, j)).r;
   dt[3] = (bm.getpixel(i + 1, j)).r;
   dt[4] = (bm.getpixel(i + 2, j)).r;
   r = median_sorter(dt, 5); //中值 dt = new int[5];


   //取5×1窗口5个像素
   dt1 = new int[5];
   dt1[0] = (bm.getpixel(i - 2, j)).g;
   dt1[1] = (bm.getpixel(i - 1, j)).g;
   dt1[2] = (bm.getpixel(i, j)).g;
   dt1[3] = (bm.getpixel(i + 1, j)).g;
   dt1[4] = (bm.getpixel(i + 2, j)).g;
   r1 = median_sorter(dt1, 5); //中值

   //取5×1窗口5个像素
   dt2 = new int[5];
   dt2[0] = (bm.getpixel(i - 2, j)).b;
   dt2[1] = (bm.getpixel(i - 1, j)).b;
   dt2[2] = (bm.getpixel(i, j)).b;
   dt2[3] = (bm.getpixel(i + 1, j)).b;
   dt2[4] = (bm.getpixel(i + 2, j)).b;
   r2 = median_sorter(dt2, 5); //中值

   }
   obm.setpixel(i, j, color.fromargb(r, r1, r2));  //输出
  }
  }
  return obm;
 }
 //冒泡排序,输出中值
 public int median_sorter(int[] dt, int m)
 {
  int tem;
  for (int k = m - 1; k >= 1; k--)
  for (int l = 1; l <= k; l++)
   if (dt[l - 1] > dt[l])
   {
   tem = dt[l];
   dt[l] = dt[l - 1];
   dt[l - 1] = tem;
   }
  return dt[(int)(m / 2)];
 }
 private void picturebox1_click(object sender, eventargs e)
 {
 }


 private void 图像锐化toolstripmenuitem_click(object sender, eventargs e)
 {
  }


 /*
  * pix --待检测图像数组
  * iw, ih --待检测图像宽高
  * num --算子代号.1:kirsch算子;2:laplace算子;3:prewitt算子;5:sobel算子
 */
 public bitmap detect(bitmap bm, int iw, int ih, int num)
  {

  bitmap b1 = new bitmap(picturebox1.image);

  color c = new color();
  int i, j, r;
  int[,] inr = new int[iw, ih]; //红色分量矩阵
  int[,] ing = new int[iw, ih]; //绿色分量矩阵
  int[,] inb = new int[iw, ih]; //蓝色分量矩阵
  int[,] gray = new int[iw, ih];//灰度图像矩阵

  //转变为灰度图像矩阵

  for (j = 0; j < ih; j++)
  {
  for (i = 0; i < iw; i++)
  {
   c = bm.getpixel(i, j);
   inr[i, j] = c.r;
   ing[i, j] = c.g;
   inb[i, j] = c.b;
   gray[i, j] = (int)((c.r + c.g + c.b) / 3.0);
  }
  }
  if (num == 1)//kirsch
  {
  int[,] kir0 = {{ 5, 5, 5},
    {-3, 0,-3},
    {-3,-3,-3}},//kir0

   kir1 = {{-3, 5, 5},
    {-3, 0, 5},
    {-3,-3,-3}},//kir1

   kir2 = {{-3,-3, 5},
    {-3, 0, 5},
    {-3,-3, 5}},//kir2

   kir3 = {{-3,-3,-3},
    {-3, 0, 5},
    {-3, 5, 5}},//kir3

   kir4 = {{-3,-3,-3},
    {-3, 0,-3},
    { 5, 5, 5}},//kir4

   kir5 = {{-3,-3,-3},
    { 5, 0,-3},
    { 5, 5,-3}},//kir5

   kir6 = {{ 5,-3,-3},
    { 5, 0,-3},
    { 5,-3,-3}},//kir6

   kir7 = {{ 5, 5,-3},
    { 5, 0,-3},
    {-3,-3,-3}};//kir7
  //边缘检测

  int[,] edge0 = new int[iw, ih];

  int[,] edge1 = new int[iw, ih];

  int[,] edge2 = new int[iw, ih];

  int[,] edge3 = new int[iw, ih];

  int[,] edge4 = new int[iw, ih];

  int[,] edge5 = new int[iw, ih];

  int[,] edge6 = new int[iw, ih];

  int[,] edge7 = new int[iw, ih];

  edge0 = edgeenhance(gray, kir0, iw, ih);
  edge1 = edgeenhance(gray, kir1, iw, ih);
  edge2 = edgeenhance(gray, kir2, iw, ih);
  edge3 = edgeenhance(gray, kir3, iw, ih);
  edge4 = edgeenhance(gray, kir4, iw, ih);
  edge5 = edgeenhance(gray, kir5, iw, ih);
  edge6 = edgeenhance(gray, kir6, iw, ih);
  edge7 = edgeenhance(gray, kir7, iw, ih);

  int[] tem = new int[8];
  int max;
  for (j = 0; j < ih; j++)
  {
   for (i = 0; i < iw; i++)
   {
   tem[0] = edge0[i, j];
   tem[1] = edge1[i, j];
   tem[2] = edge2[i, j];
   tem[3] = edge3[i, j];
   tem[4] = edge4[i, j];
   tem[5] = edge5[i, j];
   tem[6] = edge6[i, j];
   tem[7] = edge7[i, j];
   max = 0;
   for (int k = 0; k < 8; k++)
    if (tem[k] > max) max = tem[k];
   if (max > 255) max = 255;
   r = 255 - max;
   b1.setpixel(i, j, color.fromargb(r, r, r));
   }
  }
  }
  else if (num == 2)   //laplace
  {
  int[,] lap1 = {{ 1, 1, 1},
    { 1,-8, 1},
    { 1, 1, 1}};

  /*byte[][] lap2 = {{ 0, 1, 0},
     { 1,-4, 1},
     { 0, 1, 0}}; */

  //边缘增强
  int[,] edge = edgeenhance(gray, lap1, iw, ih);

  for (j = 0; j < ih; j++)
  {
   for (i = 0; i < iw; i++)
   {
   r = edge[i, j];
   if (r > 255) r = 255;

   if (r < 0) r = 0;
   c = color.fromargb(r, r, r);
   b1.setpixel(i, j, c);
   }
  }
  }
  else if (num == 3)//prewitt
  {
  //prewitt算子d_x模板
  int[,] pre1 = {{ 1, 0,-1},
    { 1, 0,-1},
    { 1, 0,-1}};

  //prewitt算子d_y模板
  int[,] pre2 = {{ 1, 1, 1},
    { 0, 0, 0},
    {-1,-1,-1}};
  int[,] edge1 = edgeenhance(gray, pre1, iw, ih);

  int[,] edge2 = edgeenhance(gray, pre2, iw, ih);
  for (j = 0; j < ih; j++)
  {
   for (i = 0; i < iw; i++)
   {
   r = math.max(edge1[i, j], edge2[i, j]);

   if(r > 255) r = 255;
   c = color.fromargb(r, r, r);
   b1.setpixel(i, j, c);
   }
  }
  }

  else if (num == 5)    //sobel
  {
  int[,] sob1 = {{ 1, 0,-1},
    { 2, 0,-2},
    { 1, 0,-1}};
  int[,] sob2 = {{ 1, 2, 1},
    { 0, 0, 0},
    {-1,-2,-1}},

  int[,] edge1 = edgeenhance(gray, sob1, iw, ih);
  int[,] edge2 = edgeenhance(gray, sob2, iw, ih);
  for (j = 0; j < ih; j++)
  {
   for (i = 0; i < iw; i++)
   {
   r = math.max(edge1[i, j], edge2[i, j]);
   if(r > 255) r = 255;
   c = color.fromargb(r, r, r);
   b1.setpixel(i, j, c);
   }
  }
  }
  return b1;
 }
 private void kirsch算子锐化toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  // this.text = " 图像 - 图像锐化 - kirsch算子";
  bitmap bm = new bitmap(picturebox1.image);
  //1: kirsch锐化
  bm = detect(bm, iw, ih, 1)
  picturebox2.refresh();
  picturebox2.image = bm;
  label7.text = " kirsch算子 锐化结果";
  }
 }
 public int[,] edgeenhance(int[,] ing, int[,] tmp, int iw, int ih)
 {
  int[,] ed = new int[iw, ih];
  for (int j = 1; j < ih - 1; j++)
  {
  for (int i = 1; i < iw - 1; i++)
  {
   ed[i, j] = math.abs(tmp[0, 0] * ing[i - 1, j - 1]
    + tmp[0, 1] * ing[i - 1, j] + tmp[0, 2] * ing[i - 1, j + 1]
    + tmp[1, 0] * ing[i, j - 1] + tmp[1, 1] * ing[i, j]
    + tmp[1, 2] * ing[i, j + 1] + tmp[2, 0] * ing[i + 1, j - 1]
    + tmp[2, 1] * ing[i + 1, j] + tmp[2, 2] * ing[i + 1, j + 1]);
  }
  }
  return ed;
 }
 //laplace算子
 private void laplace算子锐化toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  bitmap bm = new bitmap(picturebox1.image);

  //2: laplace锐化
  bm = detect(bm, iw, ih, 2);
  picturebox2.refresh();
  picturebox2.image = bm;
  label7.text = "laplace算子 锐化结果";
  }
 }

 //prewitt算子
 private void prewitt算子锐化toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {

  bitmap bm = new bitmap(picturebox1.image);
  //3:prewitt锐化
  bm = detect(bm, iw, ih, 3);
  picturebox2.refresh();
  picturebox2.image = bm;
  label2.location = new point(390, 280);
  label7.text = " prewitt算子 锐化结果";
  }
 }


 //roberts算子
 private void roberts算子锐化toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  bitmap bm = new bitmap(picturebox1.image);
  //robert边缘检测
  bm = robert(bm, iw, ih);
  picturebox2.refresh();
  picturebox2.image = bm;
  label2.location = new point(390, 280);
  label7.text = "roberts算子 锐化结果";
  }
 }

 //roberts算法
 public bitmap robert(bitmap bm, int iw, int ih)
 {
  int r, r0, r1, r2, r3, g, g0, g1, g2, g3, b, b0, b1, b2, b3;
  bitmap obm = new bitmap(picturebox1.image);
  int[,] inr = new int[iw, ih];//红色分量矩阵
  int[,] ing = new int[iw, ih];//绿色分量矩阵
  int[,] inb = new int[iw, ih];//蓝色分量矩阵
  int[,] gray = new int[iw, ih];//灰度图像矩阵

  for (int j = 1; j < ih - 1; j++)
  {
  for (int i = 1; i < iw - 1; i++)
  {
   r0 = (bm.getpixel(i, j)).r;
   r1 = (bm.getpixel(i, j + 1)).r;
   r2 = (bm.getpixel(i + 1, j)).r;
   r3 = (bm.getpixel(i + 1, j + 1)).r;

   r = (int)math.sqrt((r0 - r3) * (r0 - r3) + (r1 - r2) * (r1 - r2));

   g0 = (bm.getpixel(i, j)).g;
   g1 = (bm.getpixel(i, j + 1)).g;
   g2 = (bm.getpixel(i + 1, j)).g;
   g3 = (bm.getpixel(i + 1, j + 1)).g;
   g = (int)math.sqrt((g0 - g3) * (g0 - g3) + (g1 - g2) * (g1 - g2));

   b0 = (bm.getpixel(i, j)).b;
   b1 = (bm.getpixel(i, j + 1)).b;
   b2 = (bm.getpixel(i + 1, j)).b;
   b3 = (bm.getpixel(i + 1, j + 1)).b;
   b = (int)math.sqrt((b0 - b3) * (b0 - b3)
   + (b1 - b2) * (b1 - b2));

   if (r < 0)
   r = 0;     //黑色，边缘点
   if (r > 255)
   r = 255;

   obm.setpixel(i, j, color.fromargb(r, r, r));
  }
  }
  return obm;
 }
 //sobel算子
 private void sobel算子锐化toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  bitmap bm = new bitmap(picturebox1.image);
  //5: sobel锐化
  bm = detect(bm, 256, 256, 5);

  picturebox2.refresh();
  picturebox2.image = bm;

  label7.text = " sobel算子 锐化结果";
  }
 }

 private void 低通滤波toolstripmenuitem_click(object sender, eventargs e)
 {
  if (bitmap != null)
  {
  bitmap bm = new bitmap(picturebox1.image);
  int num ;
  for (num = 1; num < 4; num++)
  {
   //低通滤波
   bm = lowpass(bm, iw, ih, num);

   picturebox2.refresh();
   picturebox2.image = bm;

   if (num == 1) label7.text = "1*5模板低通滤波结果";
   else if (num == 2) label7.text = "5*1模板低通滤波结果";
   else if (num == 3) label7.text = "5*5模板低通滤波结果";
  }
  }


 }
 //3×3低通滤波方法
 public bitmap lowpass(bitmap bm, int iw, int ih, int n)
 {
  bitmap obm = new bitmap(picturebox1.image);
  int[,] h;

  //定义扩展输入图像矩阵
  int[,] ex_inpix = exinpix(bm, iw, ih);

  //低通滤波
  for (int j = 1; j < ih + 1; j++)
  {
  for (int i = 1; i < iw + 1; i++)
  {
   int r = 0, sum = 0;

   //低通模板
   h = low_matrix(n);

   //求3×3窗口9个像素加权和
   for (int k = -1; k < 2; k++)
   for (int l = -1; l < 2; l++)
    sum = sum + h[k + 1, l + 1] * ex_inpix[i + k, j + l];

   if (n == 1)
   r = (int)(sum / 9); //h1平均值
   else if (n == 2)
   r = (int)(sum / 10); //h2
   else if (n == 3)
   r = (int)(sum / 16); //h3
   obm.setpixel(i - 1, j - 1, color.fromargb(r, r, r)); //输出
  }
  }
  return obm;
 }
 //定义扩展输入图像矩阵
 public int[,] exinpix(bitmap bm, int iw, int ih)
 {
  int[,] ex_inpix = new int[iw + 2, ih + 2];
  //获取非边界灰度值
  for (int j = 0; j < ih; j++)
  for (int i = 0; i < iw; i++)
   ex_inpix[i + 1, j + 1] = (bm.getpixel(i, j)).r;
  //四角点处理
  ex_inpix[0, 0] = ex_inpix[1, 1];
  ex_inpix[0, ih + 1] = ex_inpix[1, ih];
  ex_inpix[iw + 1, 0] = ex_inpix[iw, 1];
  ex_inpix[iw + 1, ih + 1] = ex_inpix[iw, ih];
  //上下边界处理
  for (int j = 1; j < ih + 1; j++)
  {
  ex_inpix[0, j] = ex_inpix[1, j]; //上边界
  ex_inpix[iw + 1, j] = ex_inpix[iw, j];//下边界
  }

//左右边界处理
  for (int i = 1; i < iw + 1; i++)
  {
  ex_inpix[i, 0] = ex_inpix[i, 1]; //左边界
  ex_inpix[i, ih + 1] = ex_inpix[i, ih];//右边界
  }
  return ex_inpix;
 }
 //低通滤波模板
 public int[,] low_matrix(int n)
 {
  int[,] h = new int[3, 3];
  if (n == 1) //h1
  {
  h[0, 0] = 1; h[0, 1] = 1; h[0, 2] = 1;
  h[1, 0] = 1; h[1, 1] = 1; h[1, 2] = 1;
  h[2, 0] = 1; h[2, 1] = 1; h[2, 2] = 1;
  }
  else if (n == 2)//h2
  {
  h[0, 0] = 1; h[0, 1] = 1; h[0, 2] = 1;
  h[1, 0] = 1; h[1, 1] = 2; h[1, 2] = 1;
  h[2, 0] = 1; h[2, 1] = 1; h[2, 2] = 1;
  }
  else if (n == 3)//h3
  {
  h[0, 0] = 1; h[0, 1] = 2; h[0, 2] = 1;
  h[1, 0] = 2; h[1, 1] = 4; h[1, 2] = 2;
  h[2, 0] = 1; h[2, 1] = 2; h[2, 2] = 1;
  }
  return h;
 }

 }
}

六、参考书籍

《c#数字图像处理算法典型实例》

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。

原文链接：https://blog.csdn.net/Lynn_whu/article/details/80725831