//高斯平滑处理方法
        //inputImage 输入图像
        // outputImage 输出图像
        //sigema 均方差
        private void gaussSmooth(double[]inputImage,out double[] outputImage,double sigema)
        {
            //方差
            double std2 = 2 * sigema * sigema;
            //半径=3sigema
            int radius = Convert.ToInt16(Math.Ceiling(3 * sigema));
            int filterWidth = 2 * radius + 1;
            double[] filter = new double[filterWidth];
            outputImage = new double[inputImage.Length];

            //限定输入的情况为方阵的情况下得到的图像的宽度和高度
            int length = Convert.ToInt16(Math.Sqrt(inputImage.Length));
            double[] tempImage = new double[inputImage.Length];

            double sum = 0;
            //产生一维高斯函数
            for (int i = 0; i < filterWidth; i++)
            {
                int xx = (i - radius) * (i - radius);
                filter[i] = Math.Exp(-xx / std2);
                sum += filter[i];
            }

            //归一化
            for (int i = 0; i < filterWidth; i++)
            {
                filter[i] = filter[i] / sum;
            }

            //水平方向滤波
            for (int i = 0; i < length; i++)
            {
                for (int j = 0; j < length; j++)
                {
                    double temp = 0;
                    for (int k = -radius; k <= radius; k++)
                    {
                        //循环拓展
                        int rem = (Math.Abs(j + k) % length);
                        //计算卷积和
                        temp += inputImage[i * length + rem] * filter[k + radius];
                    }
                    tempImage[i + length + j] = temp;
                }
            }

            //垂直方向滤波
            for (int j = 0; j < length; j++)
            {
                for (int i = 0; i < length; i++)
                {
                    double temp = 0;
                    for (int k = -radius ; k <=radius; k++)
                    {
                        //循环拓展
                        int rem = (Math.Abs(i + k) )% length;

                        //计算卷积和
                        temp += tempImage[rem * length + j] * filter[k + radius];
                    }
                    outputImage[i * length + j] = temp;
                }
            }
        }