不均匀光照二值化算法 sauvola

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void sauvola(unsigned char * grayImage, unsigned char * biImage, int w, int h, double k, int windowSize){int whalf = windowSize >> 1;int i, j;int IMAGE_WIDTH = w;int IMAGE_HEIGHT = h;// create the integral imageunsigned long * integralImg = (unsigned long*)malloc(IMAGE_WIDTH*IMAGE_HEIGHT * sizeof(unsigned long*));unsigned long * integralImgSqrt = (unsigned long*)malloc(IMAGE_WIDTH*IMAGE_HEIGHT * sizeof(unsigned long*));int sum = 0;int sqrtsum = 0;int index;for (i = 0; i < IMAGE_HEIGHT; i++){// reset this column sumsum = 0;sqrtsum = 0;for (j = 0; j < IMAGE_WIDTH; j++){index = i*IMAGE_WIDTH + j;sum += grayImage[index];sqrtsum += grayImage[index] * grayImage[index];if (i == 0){integralImg[index] = sum;integralImgSqrt[index] = sqrtsum;}else{integralImgSqrt[index] = integralImgSqrt[(i - 1)*IMAGE_WIDTH + j] + sqrtsum;integralImg[index] = integralImg[(i - 1)*IMAGE_WIDTH + j] + sum;}}}//Calculate the mean and standard deviation using the integral imageint xmin, ymin, xmax, ymax;double mean, std, threshold;double diagsum, idiagsum, diff, sqdiagsum, sqidiagsum, sqdiff, area;for (i = 0; i < IMAGE_WIDTH; i++) {for (j = 0; j < IMAGE_HEIGHT; j++) {xmin = max(0, i - whalf);ymin = max(0, j - whalf);xmax = min(IMAGE_WIDTH - 1, i + whalf);ymax = min(IMAGE_HEIGHT - 1, j + whalf);area = (xmax - xmin + 1) * (ymax - ymin + 1);if (area <= 0){biImage[i * IMAGE_WIDTH + j] = 255;continue;}if (xmin == 0 && ymin == 0) {diff = integralImg[ymax * IMAGE_WIDTH + xmax];sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax];}else if (xmin > 0 && ymin == 0) {diff = integralImg[ymax * IMAGE_WIDTH + xmax] - integralImg[ymax * IMAGE_WIDTH + xmin - 1];sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] - integralImgSqrt[ymax * IMAGE_WIDTH + xmin - 1];}else if (xmin == 0 && ymin > 0) {diff = integralImg[ymax * IMAGE_WIDTH + xmax] - integralImg[(ymin - 1) * IMAGE_WIDTH + xmax];sqdiff = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] - integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmax];;}else {diagsum = integralImg[ymax * IMAGE_WIDTH + xmax] + integralImg[(ymin - 1) * IMAGE_WIDTH + xmin - 1];idiagsum = integralImg[(ymin - 1) * IMAGE_WIDTH + xmax] + integralImg[ymax * IMAGE_WIDTH + xmin - 1];diff = diagsum - idiagsum;sqdiagsum = integralImgSqrt[ymax * IMAGE_WIDTH + xmax] + integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmin - 1];sqidiagsum = integralImgSqrt[(ymin - 1) * IMAGE_WIDTH + xmax] + integralImgSqrt[ymax * IMAGE_WIDTH + xmin - 1];sqdiff = sqdiagsum - sqidiagsum;}mean = diff / area;std = sqrt((sqdiff - diff*diff / area) / (area - 1));threshold = mean*(1 + k*((std / 128) - 1));if (grayImage[j*IMAGE_WIDTH + i] < threshold)biImage[j*IMAGE_WIDTH + i] = 0;elsebiImage[j*IMAGE_WIDTH + i] = 255;}}free(integralImg);free(integralImgSqrt);}

测试

sauvola(srcImg.data, dstImg.data, srcImg.cols, srcImg.rows,0.3 , 40);

windowsize: 以当前像素点为中心的邻域的宽度

k :使用者自定义的修正系数

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