圖像處理之基于閾值模糊

算法思想：

實現一個高斯卷積模糊但是只運用與周圍的像素值與中心像素值差值小于閾值。兩個

像素值之間的距離計算可以選用向量距離即曼哈頓距離或者歐幾里德距離。高斯模糊

采用先XY方向一維高斯模糊完成目的是為了減小計算量。

程序效果：

關鍵代碼解釋：

分別完成XY方向的一維高斯模糊

thresholdBlur( kernel, inPixels, outPixels, width, height, true ); thresholdBlur( kernel, outPixels, inPixels, height, width, true );計算像素距離，完成像素高斯卷積代碼如下：

int d; if(euclid) { d = (int)Math.sqrt(a1*a1-a2*a2); } else { d = a1-a2; } if ( d >= -threshold && d <= threshold ) { a += f * a2; af += f; } if(euclid) { d = (int)Math.sqrt(r1*r1-r2*r2); } else { d = r1-r2; } if ( d >= -threshold && d <= threshold ) { r += f * r2; rf += f; } if(euclid) { d = (int)Math.sqrt(g1*g1-g2*g2); } else { d = g1-g2; } if ( d >= -threshold && d <= threshold ) { g += f * g2; gf += f; } if(euclid) { d = (int)Math.sqrt(b1*b1-b2*b2); } else { d = b1-b2; } if ( d >= -threshold && d <= threshold ) { b += f * b2; bf += f; }濾鏡完整代碼如下：

package com.gloomyfish.filter.study; import java.awt.image.BufferedImage; public class SmartBlurFilter extends AbstractBufferedImageOp { private int hRadius = 5; private int threshold = 50; private boolean euclid = false; public BufferedImage filter( BufferedImage src, BufferedImage dest ) { int width = src.getWidth(); int height = src.getHeight(); if ( dest == null ) dest = createCompatibleDestImage( src, null ); int[] inPixels = new int[width*height]; int[] outPixels = new int[width*height]; getRGB( src, 0, 0, width, height, inPixels ); // generate the Gaussian kernel data float[] kernel = makeKernel(hRadius); // do Gaussian X and Y direction with kernel data. // this way will proceed quickly thresholdBlur( kernel, inPixels, outPixels, width, height, true ); thresholdBlur( kernel, outPixels, inPixels, height, width, true ); // set back result data to destination image setRGB( dest, 0, 0, width, height, inPixels ); return dest; } /** * Convolve with a Gaussian matrix consisting of one row float data */ public void thresholdBlur(float[] matrix, int[] inPixels, int[] outPixels, int width, int height, boolean alpha) { int cols = matrix.length; int cols2 = cols/2; for (int y = 0; y < height; y++) { int ioffset = y*width; // index to correct row here!! int outIndex = y; for (int x = 0; x < width; x++) { float r = 0, g = 0, b = 0, a = 0; int moffset = cols2; int rgb1 = inPixels[ioffset+x]; int a1 = (rgb1 >> 24) & 0xff; int r1 = (rgb1 >> 16) & 0xff; int g1 = (rgb1 >> 8) & 0xff; int b1 = rgb1 & 0xff; float af = 0, rf = 0, gf = 0, bf = 0; for (int col = -cols2; col <= cols2; col++) { float f = matrix[moffset+col]; if (f != 0) { int ix = x+col; if (!(0 <= ix && ix < width)) ix = x; int rgb2 = inPixels[ioffset+ix]; int a2 = (rgb2 >> 24) & 0xff; int r2 = (rgb2 >> 16) & 0xff; int g2 = (rgb2 >> 8) & 0xff; int b2 = rgb2 & 0xff; int d; if(euclid) { d = (int)Math.sqrt(a1*a1-a2*a2); } else { d = a1-a2; } if ( d >= -threshold && d <= threshold ) { a += f * a2; af += f; } if(euclid) { d = (int)Math.sqrt(r1*r1-r2*r2); } else { d = r1-r2; } if ( d >= -threshold && d <= threshold ) { r += f * r2; rf += f; } if(euclid) { d = (int)Math.sqrt(g1*g1-g2*g2); } else { d = g1-g2; } if ( d >= -threshold && d <= threshold ) { g += f * g2; gf += f; } if(euclid) { d = (int)Math.sqrt(b1*b1-b2*b2); } else { d = b1-b2; } if ( d >= -threshold && d <= threshold ) { b += f * b2; bf += f; } } } // normalization process here a = af == 0 ? a1 : a/af; r = rf == 0 ? r1 : r/rf; g = gf == 0 ? g1 : g/gf; b = bf == 0 ? b1 : b/bf; // return result pixel data int ia = alpha ? PixelUtils.clamp((int)(a+0.5)) : 0xff; int ir = PixelUtils.clamp((int)(r+0.5)); int ig = PixelUtils.clamp((int)(g+0.5)); int ib = PixelUtils.clamp((int)(b+0.5)); outPixels[outIndex] = (ia << 24) | (ir << 16) | (ig << 8) | ib; outIndex += height; } } } public void setHRadius(int hRadius) { this.hRadius = hRadius; } public void setThreshold(int th) { this.threshold = th; } public void setEuclid(boolean apply) { this.euclid = apply; } }

轉載于:https://blog.51cto.com/gloomyfish/1400330

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