基于优化对比度增强的图像去雾算法
来源：互联网 发布：政治工作 网络关 编辑：程序博客网 时间：2024/06/05 18:04
新年没看见什么新气象，可能雾霾太大，真个望长城内外，惟余莽莽，于是想起写一篇有关去雾的博客吧。图像去雾的最大挑战是：去雾的同时，能够保持图像不偏色，并且速度要快。本文算法来自《 Optimized contrast enhancement for real-time image and video dehazing》这篇文章，在实际测试中效果还可以。算法原理同样基于大气散射模型：
算法流程如下图所示：
1. 估算大气环境光：算法采用图像分块策略，递归查找大气环境光所在的图像块，定位到图像块后，在遍历图像块内像素，根据下述公式：
计算大气环境光数值。
void EstimateAirlight(BMPINFO *pSrcBitmap, uchar *airlight){int blockMaxValIndex = 0;float blockCurVal = 0.0f;float blockMaxVal = 0.0f;float mean[3] = { 0.0f };float stddev[3] = { 0.0f };float channelVal[3] = { 0.0f };int width = pSrcBitmap->lWidth;int height = pSrcBitmap->lHeight;int size = width * height;int halfWidth = width / 2;int halfHeight = height / 2;uchar *pSrcData = NULL;uchar *pBlockData = NULL;BMPINFO upperLeftBitmap = { 0 }, upperRightBitmap = { 0 }, lowerLeftBitmap = { 0 }, lowerRightBitmap = { 0 };if (size > 400){// 分块并填充数据// 左上upperLeftBitmap.dwPixelFormat = BMPFORMAT_RGB32_R8G8B8A8;upperLeftBitmap.lWidth = halfWidth;upperLeftBitmap.lHeight = halfHeight;upperLeftBitmap.lPitch[0] = upperLeftBitmap.lWidth * 4;upperLeftBitmap.pPlane[0] = (uchar *)malloc(upperLeftBitmap.lPitch[0] * upperLeftBitmap.lHeight);pSrcData = pSrcBitmap->pPlane[0];pBlockData = upperLeftBitmap.pPlane[0];for (int i = 0; i < upperLeftBitmap.lHeight; i++){memcpy(pBlockData, pSrcData, upperLeftBitmap.lPitch[0]);pBlockData += upperLeftBitmap.lPitch[0];pSrcData += pSrcBitmap->lPitch[0];}// 右上upperRightBitmap.dwPixelFormat = BMPFORMAT_RGB32_R8G8B8A8;upperRightBitmap.lWidth = width - halfWidth;upperRightBitmap.lHeight = halfHeight;upperRightBitmap.lPitch[0] = upperRightBitmap.lWidth * 4;upperRightBitmap.pPlane[0] = (uchar *)malloc(upperRightBitmap.lPitch[0] * upperRightBitmap.lHeight);pSrcData = pSrcBitmap->pPlane[0] + halfWidth*4;pBlockData = upperRightBitmap.pPlane[0];for (int i = 0; i < upperRightBitmap.lHeight; i++){memcpy(pBlockData, pSrcData, upperRightBitmap.lPitch[0]);pBlockData += upperRightBitmap.lPitch[0];pSrcData += pSrcBitmap->lPitch[0];}// 左下lowerLeftBitmap.dwPixelFormat = BMPFORMAT_RGB32_R8G8B8A8;lowerLeftBitmap.lWidth = halfWidth;lowerLeftBitmap.lHeight = height - halfHeight;lowerLeftBitmap.lPitch[0] = lowerLeftBitmap.lWidth * 4;lowerLeftBitmap.pPlane[0] = (uchar *)malloc(lowerLeftBitmap.lPitch[0] * lowerLeftBitmap.lHeight);pSrcData = pSrcBitmap->pPlane[0] + pSrcBitmap->lPitch[0]*halfHeight;pBlockData = lowerLeftBitmap.pPlane[0];for (int i = 0; i < lowerLeftBitmap.lHeight; i++){memcpy(pBlockData, pSrcData, lowerLeftBitmap.lPitch[0]);pBlockData += lowerLeftBitmap.lPitch[0];pSrcData += pSrcBitmap->lPitch[0];}// 右下lowerRightBitmap.dwPixelFormat = BMPFORMAT_RGB32_R8G8B8A8;lowerRightBitmap.lWidth = width - halfWidth;lowerRightBitmap.lHeight = height - halfHeight;lowerRightBitmap.lPitch[0] = lowerRightBitmap.lWidth * 4;lowerRightBitmap.pPlane[0] = (uchar *)malloc(lowerRightBitmap.lPitch[0] * lowerRightBitmap.lHeight);pSrcData = pSrcBitmap->pPlane[0] + pSrcBitmap->lPitch[0]*halfHeight + halfWidth*4;pBlockData = lowerRightBitmap.pPlane[0];for (int i = 0; i < lowerRightBitmap.lHeight; i++){memcpy(pBlockData, pSrcData, lowerRightBitmap.lPitch[0]);pBlockData += lowerRightBitmap.lPitch[0];pSrcData += pSrcBitmap->lPitch[0];}// 计算最优块值// 左上CompMean_StdDev(&upperLeftBitmap, mean, stddev);channelVal[0] = mean[0] - stddev[0];channelVal[1] = mean[1] - stddev[1];channelVal[2] = mean[2] - stddev[2];blockCurVal = channelVal[0] + channelVal[1] + channelVal[2];blockMaxVal = blockCurVal;blockMaxValIndex = 0;// 右上CompMean_StdDev(&upperRightBitmap, mean, stddev);channelVal[0] = mean[0] - stddev[0];channelVal[1] = mean[1] - stddev[1];channelVal[2] = mean[2] - stddev[2];blockCurVal = channelVal[0] + channelVal[1] + channelVal[2];if (blockCurVal > blockMaxVal){blockMaxVal = blockCurVal;blockMaxValIndex = 1;}// 左下CompMean_StdDev(&lowerLeftBitmap, mean, stddev);channelVal[0] = mean[0] - stddev[0];channelVal[1] = mean[1] - stddev[1];channelVal[2] = mean[2] - stddev[2];blockCurVal = channelVal[0] + channelVal[1] + channelVal[2];if (blockCurVal > blockMaxVal){blockMaxVal = blockCurVal;blockMaxValIndex = 2;}// 右下CompMean_StdDev(&lowerRightBitmap, mean, stddev);channelVal[0] = mean[0] - stddev[0];channelVal[1] = mean[1] - stddev[1];channelVal[2] = mean[2] - stddev[2];blockCurVal = channelVal[0] + channelVal[1] + channelVal[2];if (blockCurVal > blockMaxVal){blockMaxVal = blockCurVal;blockMaxValIndex = 3;}// 递归查找大气环境光所在的块switch (blockMaxValIndex){case 0:free(upperRightBitmap.pPlane[0]);upperRightBitmap.pPlane[0] = NULL;free(lowerLeftBitmap.pPlane[0]);lowerLeftBitmap.pPlane[0] = NULL;free(lowerRightBitmap.pPlane[0]);lowerRightBitmap.pPlane[0] = NULL;EstimateAirlight(&upperLeftBitmap, airlight);break;case 1:free(upperLeftBitmap.pPlane[0]);upperLeftBitmap.pPlane[0] = NULL;free(lowerLeftBitmap.pPlane[0]);lowerLeftBitmap.pPlane[0] = NULL;free(lowerRightBitmap.pPlane[0]);lowerRightBitmap.pPlane[0] = NULL;EstimateAirlight(&upperRightBitmap, airlight);break;case 2:free(upperLeftBitmap.pPlane[0]);upperLeftBitmap.pPlane[0] = NULL;free(upperRightBitmap.pPlane[0]);upperRightBitmap.pPlane[0] = NULL;free(lowerRightBitmap.pPlane[0]);lowerRightBitmap.pPlane[0] = NULL;EstimateAirlight(&lowerLeftBitmap, airlight);break;case 3:free(upperLeftBitmap.pPlane[0]);upperLeftBitmap.pPlane[0] = NULL;free(upperRightBitmap.pPlane[0]);upperRightBitmap.pPlane[0] = NULL;free(lowerLeftBitmap.pPlane[0]);lowerLeftBitmap.pPlane[0] = NULL;EstimateAirlight(&lowerRightBitmap, airlight);break;default:break;}}else{int minDistance = 99999;int eucDistance = 0;pSrcData = pSrcBitmap->pPlane[0];// 估算大气环境光for (int i = 0; i < size; i++, pSrcData += 4){eucDistance = int(sqrt((255.0f - pSrcData[AXJ_BLUE])*(255.0f - pSrcData[AXJ_BLUE]) + (255.0f - pSrcData[AXJ_GREEN])*(255.0f - pSrcData[AXJ_GREEN]) + (255.0f - pSrcData[AXJ_RED])*(255.0f - pSrcData[AXJ_RED])));if (eucDistance < minDistance){minDistance = eucDistance;airlight[AXJ_BLUE] = pSrcData[AXJ_BLUE];airlight[AXJ_GREEN] = pSrcData[AXJ_GREEN];airlight[AXJ_RED] = pSrcData[AXJ_RED];}}}// 释放资源if (upperLeftBitmap.pPlane[0] != NULL){free(upperLeftBitmap.pPlane[0]);upperLeftBitmap.pPlane[0] = NULL;}if (upperRightBitmap.pPlane[0] != NULL){free(upperRightBitmap.pPlane[0]);upperRightBitmap.pPlane[0] = NULL;}if (lowerLeftBitmap.pPlane[0] != NULL){free(lowerLeftBitmap.pPlane[0]);lowerLeftBitmap.pPlane[0] = NULL;}if (lowerRightBitmap.pPlane[0] != NULL){free(lowerRightBitmap.pPlane[0]);lowerRightBitmap.pPlane[0] = NULL;}} 2. 估算大气透射率：这应该是本文的核心部分了，所谓优化对比度去雾，即尽量提高有雾图像的对比度，同时尽量减少图像信息损失。优化对比度计算公式：
信息量损失计算公式：
最后选取合适的参数，以获取最优透射率图：
3. 修正透射率图：本文在计算透射率图时，是以图像块而非像素点为单位计算，因此计算得到的透射率图类似马赛克效果，于是作者采用导向滤波的方式修正透射率图，即以原图的灰度图为导向图，以透射率图为滤波输入图，导向滤波结果即为修正的透射率图。导向滤波也是个挺神奇的算法，以后再讲。另外需要注意的是图像块大小也将影响最后的去雾效果。
4. 复原图像：大气环境光和透射率图，根据前面提到的大气散射模型就可以复原图像了。复原过程中可以适当做一下gamma校正，避免复原后的图像过于昏暗。下面是去雾主代码示例，自己在实现算法时，在效果和速度上肯定需要做些权衡，所以算法流程和原算法相比还是有些差异的。
void ImageDeHazing(BMPINFO *pSrcBitmap){// 原图下采样int width = 1000, height = 750;if (pSrcBitmap->lWidth > pSrcBitmap->lHeight){width = (pSrcBitmap->lWidth > 1000) ? 1000 : 500;height = width*pSrcBitmap->lHeight / pSrcBitmap->lWidth;}else{height = (pSrcBitmap->lHeight > 1000) ? 1000 : 500;width = height*pSrcBitmap->lWidth / pSrcBitmap->lHeight;}int size = width * height;BMPINFO dSamplingBmp = { 0 };dSamplingBmp.dwPixelFormat = BMPFORMAT_RGB32_R8G8B8A8;dSamplingBmp.lWidth = pSrcBitmap->lWidth;dSamplingBmp.lHeight = pSrcBitmap->lHeight;dSamplingBmp.lPitch[0] = dSamplingBmp.lWidth * 4;dSamplingBmp.pPlane[0] = (uchar*)malloc(dSamplingBmp.lPitch[0] * dSamplingBmp.lHeight);memcpy(dSamplingBmp.pPlane[0], pSrcBitmap->pPlane[0], dSamplingBmp.lPitch[0]*pSrcBitmap->lHeight);ImageResampling(&dSamplingBmp, width, height);float *pGrayData = (float *)malloc(size * sizeof(float));uchar *pSrcData = NULL, *pDstData = NULL;pSrcData = dSamplingBmp.pPlane[0];for (int i = 0; i < size; i++, pSrcData += 4){pGrayData[i] = (pSrcData[AXJ_BLUE] + pSrcData[AXJ_GREEN] + pSrcData[AXJ_RED]) / 3.0f;}// 估算大气环境光uchar airlight[3] = { 0 };EstimateAirlight(&dSamplingBmp, airlight);// 估算大气透射率const int blockSize = 5;float *pAirTransmission = (float *)malloc(width * height * sizeof(float));EstimateTransmission(&dSamplingBmp, airlight, pAirTransmission, blockSize, blockSize);// 修正透射率图BMPINFO transbmp = { 0 };transbmp.dwPixelFormat = BMPFORMAT_GRAY8;transbmp.lWidth = width;transbmp.lHeight = height;transbmp.lPitch[0] = transbmp.lWidth;transbmp.pPlane[0] = (uchar *)malloc(transbmp.lPitch[0] * transbmp.lHeight);FastGuidedFilter(pGrayData, pAirTransmission, transbmp.pPlane[0], width, height, 40, 0.01f);// 释放资源free(pGrayData);pGrayData = NULL;free(pAirTransmission);pAirTransmission = NULL;free(dSamplingBmp.pPlane[0]);dSamplingBmp.pPlane[0] = NULL;// 输出校正uchar ret_gammaLut[256];for(int i = 0; i < 256; i++){ret_gammaLut[i] = (uchar)(pow((float)i / 255.0f, 0.65f) * 255.0f);}// 复原图像float *horLookupTable = (float *)malloc(pSrcBitmap->lWidth * sizeof(float));float *verLookupTable = (float *)malloc(pSrcBitmap->lHeight * sizeof(float));for (int i = 0; i < pSrcBitmap->lWidth; i++){horLookupTable[i] = ((float)i / pSrcBitmap->lWidth) * width;}for (int i = 0; i < pSrcBitmap->lHeight; i++){verLookupTable[i] = ((float)i / pSrcBitmap->lHeight) * height;}uchar transmission = 0;pSrcData = pSrcBitmap->pPlane[0];for (int i = 0; i < pSrcBitmap->lHeight; i++){for (int j = 0; j < pSrcBitmap->lWidth; j++, pSrcData += 4){BilinearInterGray(transbmp.pPlane[0], horLookupTable[j], verLookupTable[i], transbmp.lWidth, transbmp.lHeight, &transmission);pSrcData[AXJ_BLUE] = ret_gammaLut[(uchar)CLAMP0255((float)(pSrcData[AXJ_BLUE] - airlight[AXJ_BLUE])*255 / transmission + airlight[AXJ_BLUE])];pSrcData[AXJ_GREEN] = ret_gammaLut[(uchar)CLAMP0255((float)(pSrcData[AXJ_GREEN] - airlight[AXJ_GREEN])*255 / transmission + airlight[AXJ_GREEN])];pSrcData[AXJ_RED] = ret_gammaLut[(uchar)CLAMP0255((float)(pSrcData[AXJ_RED] - airlight[AXJ_RED])*255 / transmission + airlight[AXJ_RED])];}}// 释放资源free(horLookupTable);horLookupTable = NULL;free(verLookupTable);verLookupTable = NULL;free(transbmp.pPlane[0]);transbmp.pPlane[0] = NULL;}下面是一些去雾效果图：
原文献代码下载链接：http://download.csdn.net/detail/u013085897/9776286
参考资料：
http://mcl.korea.ac.kr/projects/dehazing/#userconsent#
http://www.cnblogs.com/Imageshop/p/3925461.html
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