高斯滤波代码

#region 二维高斯滤波
        //高斯滤波器
        private double[,] gaussFilter(int size,double sigma)
        {
            double[,] arr=new double[size,size];
            double sum = 0.0;
            int center = size; //以第一个点的坐标为原点，求出中心点的坐标
            for (int i = 0; i < size; ++i)
                for (int j = 0; j < size; ++j) 
                    sum += arr[i,j] = Math.Exp(-((i - center)*(i - center) + (j - center)*(j - center)) / (2 * sigma*sigma));
            for (int i = 0; i < size; ++i)
                for (int j = 0; j < size; ++j)
                    arr[i,j] /= sum;
            return arr;
            //for (int i = 0; i < size; ++i) {
            //    for (int j = 0; j < size; ++j)
            //        Console.Write(arr[i,j]+" ");
            //    Console.WriteLine();
            //}
        }
        //高斯滤波算法
        private void myGaussFilter(short[,] data)
        {
            int w_Data = data.GetLength(0);
            int h_Data = data.GetLength(1);
            double[,] arr=gaussFilter(3,1.5);
            for (int i = 2; i < w_Data-1; i++)
            {
                for (int j = 2; j < h_Data-1; j++)
                {
                    bool judgeBool = false;
                    for (int a = i - 1; a <= i + 1; a++)
                    {
                        for (int b = j - 1; b <= j + 1; b++)
                        {
                            if (Math.Abs(a - i) == 1 || Math.Abs(b - j) == 1)
                            {
                                if (data[a, b] == 0)
                                {
                                    judgeBool=true;
                                    break;
                                }
                            }
                        }
                        if (judgeBool)
                        {
                            break;
                        }
                    }
                    if (judgeBool)
                    {
                        //if (data[i, j] > 0)
                        //{
                        //    short[,] shortTmp = (short[,])data.Clone();
                        //    data[i, j] = edgeBianTong(shortTmp, new Point(i, j), arr);
                        //}
                        continue;
                    }
                    double tmpValue = 0;
                    for (int x = 0; x < 3; x++)
                    {
                        for (int y = 0; y < 3; y++)
                        {
                            tmpValue += data[i + 1 - x, j + 1 - y] * arr[x, y];//高斯滤波矩阵是对称的
                            
                        }
                    }
                    data[i, j] = (short)tmpValue;
                }
            }
        }
        private void myGaussFilter123(short[,] data)
        {
            int w_Data = data.GetLength(0);
            int h_Data = data.GetLength(1);
            double[,] arr = gaussFilter(3, 1.5);
            for (int i = 2; i < w_Data - 1; i++)
            {
                for (int j = 2; j < h_Data - 1; j++)
                {
                    bool judgeBool = false;
                    for (int a = i - 1; a <= i + 1; a++)
                    {
                        for (int b = j - 1; b <= j + 1; b++)
                        {
                            if (Math.Abs(a - i) == 1 || Math.Abs(b - j) == 1)
                            {
                                if (data[a, b] == 0)
                                {
                                    judgeBool = true;
                                    break;
                                }
                            }
                        }
                        if (judgeBool)
                        {
                            break;
                        }
                    }
                    if (judgeBool)
                    {
                        if (data[i, j] > 0)
                        {
                            short[,] shortTmp = (short[,])data.Clone();
                            data[i, j] = edgeBianTong(shortTmp, new Point(i, j), arr);
                        }
                        continue;
                    }
                    double tmpValue = 0;
                    for (int x = 0; x < 3; x++)
                    {
                        for (int y = 0; y < 3; y++)
                        {
                            tmpValue += data[i + 1 - x, j + 1 - y] * arr[x, y];//高斯滤波矩阵是对称的

                        }
                    }
                    data[i, j] = (short)tmpValue;
                }
            }
        }
        //边界变通
        private short edgeBianTong(short[,] data, Point point, double[,] arr)
        {
            //扩充边界和高斯滤波同时进行
            double tmpValue = 0;
            for (int i = point.X - 1 ,a=0; i <= point.X + 1; i++,a++)
            {
                for (int j = point.Y - 1 ,b=0; j <= point.Y + 1; j++,b++)
                {
                    if (data[i, j] == 0)
                    {
                        data[i, j] = data[point.X,point.Y];
                    }
                    //tmpValue += data[i, j] * arr[1-Math.Abs(i - point.X), 1-Math.Abs(j - point.Y)];
                    tmpValue += data[i, j] * arr[a,b];
                }
            }
            return (short)tmpValue;
        }
        #endregion

注：

　　1.本例子是红外图像做差得到的人体图像，非人体图像温度数值都为0.

　　2.myGaussFilter123含边界点（未全包含：整幅图像的边界未包含，仅涵盖了图像（不不包括图像边界）中的人体边缘点）。

　　3.myGaussFilter不含边界点，经测试发现，对边界去毛边并未有区别（也许与自己扩充边界用原值复制相关，有待进一步测试），所以对于整幅图像的边界点不再进行高斯滤波处理了。

　　4.不过对于颗粒度有点强的图像，用高斯滤波圆润挺好的。