快速均值模糊算法

前段时间在网上看到一个快速均值模糊算法，性能很不错。

源博客：

http://www.lellansin.com/super-fast-blur-%E6%A8%A1%E7%B3%8A%E7%AE%97%E6%B3%95.html

博主对其进行了简单的bug修正以及性能优化。

在博主机子上使用该算法对一张5000x3000的图片进行模糊处理，仅需500-600毫秒，速度非常快。

代码如下：

+ View Code?

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/*   * Super Fast Blur v1.1+   * Original author: Mario Klingemann (C++ version) * Original address: http://incubator.quasimondo.com/processing/superfastblur.pde   * C version updated by Lellansin (http://www.lellansin.com) * C version bugfix and performance optimization by tntmonks (http://tntmonks.cnblogs.com) */   void superFastBlur(unsigned char *pix, unsigned int w, unsigned int h, unsigned int comp, int radius) {     unsigned int div;     unsigned int wm, hm, wh;     unsigned int *vMIN, *vMAX;     unsigned char *r, *g, *b, *dv;     unsigned int rsum, gsum, bsum;     unsigned int p, p1, p2, yi, yw;       int x, y, i, yp;     if (radius < 1)         return;     wm = w - 1;     hm = h - 1;     wh = w * h;     div = radius + radius + 1;     vMIN = (unsigned int *)malloc(sizeof(unsigned int) * max(w, h));     vMAX = (unsigned int *)malloc(sizeof(unsigned int) * max(w, h));     r = (unsigned char *)malloc(sizeof(unsigned char) * wh);     g = (unsigned char *)malloc(sizeof(unsigned char) * wh);     b = (unsigned char *)malloc(sizeof(unsigned char) * wh);     dv = (unsigned char *)malloc(sizeof(unsigned char) * 256 * div);         for (i = 0; i < 256 * div; i++)           dv[i] = (i / div);         yw = yi = 0;     for (y = 0; y < h; y++)     {         rsum = gsum = bsum = 0;         for (i = -radius; i <= radius; i++)         {             p = (yi + min(wm, max(i, 0))) * comp;             bsum += pix[p];             gsum += pix[p + 1];             rsum += pix[p + 2];         }           for (x = 0; x < w; x++)         {             r[yi] = dv[rsum];             g[yi] = dv[gsum];             b[yi] = dv[bsum];               if (y == 0)             {                 vMIN[x] = min(x + radius + 1, wm);                 vMAX[x] = max(x - radius, 0);             }             p1 = (yw + vMIN[x]) * comp;             p2 = (yw + vMAX[x]) * comp;             bsum += pix[p1] - pix[p2];             gsum += pix[p1 + 1] - pix[p2 + 1];             rsum += pix[p1 + 2] - pix[p2 + 2];             yi++;         }         yw += w;     }         for (x = 0; x < w; x++)     {         rsum = gsum = bsum = 0;         yp = -radius * w;         for (i = -radius; i <= radius; i++)         {             yi = max(0, yp) + x;             rsum += r[yi];             gsum += g[yi];             bsum += b[yi];             yp += w;         }           yi = x;         for (y = 0; y < h; y++)         {             pix[yi * comp] = (dv[bsum]);             pix[yi * comp + 1] = (dv[gsum]);             pix[yi * comp + 2] = (dv[rsum]);               if (x == 0)             {                 vMIN[y] = min(y + radius + 1, hm) * w;                 vMAX[y] = max(y - radius, 0) * w;             }             p1 = x + vMIN[y];             p2 = x + vMAX[y];               rsum += r[p1] - r[p2];             gsum += g[p1] - g[p2];             bsum += b[p1] - b[p2];             yi += w;         }     }       free(r);     free(g);     free(b);     free(vMIN);     free(vMAX);     free(dv); }

　　该算法进行简单的修改可作图像增强之用。

该算法还可以进一步优化，这个任务就交给各位看官咯。