视觉图像：对比度受限直方图均衡化CLAHE

插值加速方法：AHE方法，不管是否带有对比度限制，都需要对图像中的每个像素计算其领域及变换函数，算法耗时严重；对其进行插值加速，使算法效率有极大提升，且质量没有下降；①将图像均匀分成等份矩阵大小，常见8行8列64块；②计算每个块的直方图，CDF，变换函数；对于块的中心像素(黑色小方块)是完全符合定义的；③对其他的像素通过其邻近的四个黑色小方块的变换函数插值来获取；位于中间的像素(蓝色阴影)采用双线性插值；位于边缘的部分(绿色阴影)采用线性插值；位于角点处的部分(红色阴影)采用块所在的变换函数；

OpenCV中，CLAHE函数实现：#include#include #include using namespace cv; using namespace std;  int main(int argc, char** argv){       // 读取RBG图片，转成Lab模式       Mat bgr_image = imread("src.jpg");       if (!bgr_image.rows){              cout << "imread failed!"<< endl;              return 0;       }        Mat lab_image;       cvtColor(bgr_image, lab_image, CV_BGR2Lab);        // 提取L通道       vector lab_planes(3);       split(lab_image, lab_planes);        // CLAHE 算法       Ptr clahe = createCLAHE();       clahe->setClipLimit(4);       Mat dst;       clahe->apply(lab_planes[0], dst);       dst.copyTo(lab_planes[0]);       merge(lab_planes, lab_image);        //恢复RGB图像       Mat image_clahe;       cvtColor(lab_image, image_clahe, CV_Lab2BGR);        //打印结果       imshow("原始图片", bgr_image);       imshow("CLAHE处理", image_clahe);       waitKey();       return 0;}

CLAHE源码解析：ContrastLimitAHE .h： #ifndef _CONTRAST_LIMIT_AHE_H_#define _CONTRAST_LIMIT_AHE_H_ #include "stdafx.h"#include  using namespace std; class ContrastLimitAHE { public:        ContrastLimitAHE();        ~ContrastLimitAHE();        int m_nGridX;                /* CLAHE 宽网格的个数 */        int m_nGridY;                /* CLAHE 高网格的个数 */        unsigned char m_aLUT[256];   /* CLAHE lookup table used for scaling of input image 输入图像缩放的查找表*/               int Initial(int nMaxPixel, int nMinPixel, int uiNrBins, int nX, int nY, int nWidth, int nHeight,float fCliplimit);              int m_nWidth;                /* CLAHE 宽 */       int m_nHeight;               /* CLAHE 高 */       int m_nCliplimit;        int m_nHistBins;        int m_nGridSize_X;        int m_nGridSize_Y;        int *m_pMapArray;        int m_nGridSize;            /* Actual size of contextual regions实际周边区域大小 */        int ProcessCLAHE(unsigned char* pImage);        void MakeHistogram(unsigned char* pImage,int* pulHistogram);        void ClipHistogram(int* pulHistogram);        void MapHistogram(int* pulHistogram);        void Interpolate (unsigned char * pImage);        int m_nMaxPixel;        int m_nMinPixel;        void ShiftInterpolate(unsigned char* pImage, int nSizeX, int nSizeY, int* pulMapLU, int* pulMapRU, int *pulMapLB, int *pulMapRB);        int LineInterpolate(unsigned char* pImage, int nSizeX, int nSizeY, int* pLU, int* pRU, int* pLB, int* pRB); }; #endif

参考资料：[1] D. J. Ketcham, R. W. Lowe & J. W. Weber: Image enhancement techniques for cockpit displays. Tech. rep., Hughes Aircraft. 1974.[2] R. A. Hummel: Image Enhancement by Histogram Transformation. Computer Graphics and Image Processing 6 (1977) 184195.[3] S. M. Pizer, E. P. Amburn, J. D. Austin, et al.: Adaptive Histogram Equalization and Its Variations. Computer Vision, Graphics, and Image Processing 39 (1987) 355-368.[4] K. Zuiderveld: Contrast Limited Adaptive Histogram Equalization. In: P. Heckbert: Graphics Gems IV, Academic Press 1994, ISBN 0-12-336155-9[5] T. Sund & A. Møystad: Sliding window adaptive histogram equalization of intra-oral radiographs: effect on diagnostic quality. Dentomaxillofac Radiol. 2006 May;35(3):133-8.

灰度映射表

双线性插值加速