Win8 Metro(C#)数字图像处理--2.71Sigma平滑滤波器

[算法说明]

  Sigma平滑滤波器是构造一个模板，比如3*3大小的模板，计算这个模板对应的像素的标准差d，然后根据统计学原理，得到一个置信区间v，假设3*3模板中心像素为p，则v大小范围为[p-d,p+d]。

我们判断3*3模板中对应像素是否落在置信区间v内，对于在v内的像素，我们求取他们的均值，然后，当前模板对应的中心像素的滤波值就等于这个均值。

图像表示如下：

        /// <summary>
        /// Sigma filter.
        /// </summary>
        /// <param name="src">The source image.</param>
        /// <returns></returns>
        public static WriteableBitmap SigmaFilterProcess(WriteableBitmap src)////Sigma滤波器
        {
            if (src != null)
            {
                int w = src.PixelWidth;
                int h = src.PixelHeight;
                WriteableBitmap filterImage = new WriteableBitmap(w, h);
                byte[] temp = src.PixelBuffer.ToArray();
                byte[] tempMask = (byte[])temp.Clone();
                double[] Gray = new double[9];
                double variance = 0;
                int count = 0;
                for (int j = 1; j < h - 1; j++)
                {
                    for (int i = 1; i < w - 1; i++)
                    {
                        variance = 0;
                        count = 0;
                        int[] B = new int[9] { tempMask[i * 4 + j * w * 4], tempMask[(i - 1) * 4 + (j - 1) * w * 4], tempMask[i * 4 + (j - 1) * w * 4], tempMask[(i + 1) * 4 + (j - 1) * w * 4], tempMask[(i - 1) * 4 + j * w * 4], tempMask[(i + 1) * 4 + j * w * 4], tempMask[(i - 1) * 4 + (j + 1) * w * 4], tempMask[i * 4 + (j + 1) * w * 4], tempMask[(i + 1) * 4 + (j + 1) * w * 4] };
                        int[] G = new int[9] { tempMask[i * 4 + 1 + j * w * 4], tempMask[(i - 1) * 4 + 1 + (j - 1) * w * 4], tempMask[i * 4 + 1 + (j - 1) * w * 4], tempMask[(i + 1) * 4 + 1 + (j - 1) * w * 4], tempMask[(i - 1) * 4 + 1 + j * w * 4], tempMask[(i + 1) * 4 + 1 + j * w * 4], tempMask[(i - 1) * 4 + 1 + (j + 1) * w * 4], tempMask[i * 4 + 1 + (j + 1) * w * 4], tempMask[(i + 1) * 4 + 1 + (j + 1) * w * 4] };
                        int[] R = new int[9] { tempMask[i * 4 + 2 + j * w * 4], tempMask[(i - 1) * 4 + 2 + (j - 1) * w * 4], tempMask[i * 4 + 2 + (j - 1) * w * 4], tempMask[(i + 1) * 4 + 2 + (j - 1) * w * 4], tempMask[(i - 1) * 4 + 2 + j * w * 4], tempMask[(i + 1) * 4 + 2 + j * w * 4], tempMask[(i - 1) * 4 + 2 + (j + 1) * w * 4], tempMask[i * 4 + 2 + (j + 1) * w * 4], tempMask[(i + 1) * 4 + 2 + (j + 1) * w * 4] };
                        for (int n = 0; n < 9; n++)
                        {
                            Gray[n] = (double)B[n] * 0.114 + (double)G[n] * 0.587 + (double)R[n] * 0.299;
                        }
                        for (int m = 0; m < 9; m++)
                        {
                            variance += ((double)Gray[m] - Gray.Average()) * ((double)Gray[m] - Gray.Average()) / 9;
                        }
                        variance = Math.Sqrt(variance);
                        for (int m = 0; m < 9; m++)
                        {
                            if (Gray[m] < Gray[0]-variance && Gray[m] > variance + Gray[0])
                            {
                                R[m] = 0;
                                G[m] = 0;
                                B[m] = 0;
                            }
                            else count++;
                        }
                        temp[i * 4 + j * w * 4] = (byte)(B.Sum() / count);
                        temp[i * 4 + 1 + j * w * 4] = (byte)(G.Sum() /count);
                        temp[i * 4 + 2 + j * w * 4] = (byte)(R.Sum() / count);
                    }
                }
                Stream sTemp = filterImage.PixelBuffer.AsStream();
                sTemp.Seek(0, SeekOrigin.Begin);
                sTemp.Write(temp, 0, w * 4 * h);
                return filterImage;
            }
            else
            {
                return null;
            }
        }