not so fair gaussian blur with box filter

gistfile1.txt

        public byte[] blurGauss_2(byte[] originalImage, int w, int h, double r)
        {
            byte[] blurImage = new byte[originalImage.Length];
            var bxs = boxesForGauss(r, 3);
            boxBlur_2(originalImage, blurImage, w, h, (bxs[0] - 1) / 2);
            boxBlur_2(blurImage, originalImage, w, h, (bxs[1] - 1) / 2);
            boxBlur_2(originalImage, blurImage, w, h, (bxs[2] - 1) / 2);


            return blurImage;
        }

        // standard deviation, number of boxes
        private int[] boxesForGauss(double sigma, int n){

            double wIdeal = Math.Sqrt((12 * sigma * sigma / n) + 1);// Ideal averaging filter width 
            int wl = (int)Math.Floor(wIdeal); if (wl % 2 == 0) wl--;
            int wu = wl+2;
            double mIdeal = (12 * sigma * sigma - n * wl * wl - 4 * n * wl - 3 * n) / (-4 * wl - 4);
            int m = (int)Math.Round(mIdeal);
            int[] sizes = new int[n];
            for (int i = 0; i < n; i++ )
            {
                sizes[i] = (i< m ? wl : wu);
            }
            return sizes;
        }

        private void boxBlur_2(byte[] originalImage, byte[] blurImage, int w, int h, int r){
            for (int i = 0; i < h; i++ )
            {
                for (int j = 0; j < w; j++) {
                    double val = 0;
                    for (int iy = i - r; iy < i + r + 1; iy++ )
                    {
                        for (int ix = j - r; ix < j + r + 1; ix++ )
                        {
                            int x = Math.Min(w-1, Math.Max(0,ix));
                            int y = Math.Min(h - 1, Math.Max(0, iy));
                            val += originalImage[y * w + x];
                        }
                    }
                    blurImage[i * w + j] = (byte)(val / ((r + r + 1) * (r + r + 1)));
                }
            }
           
        }