gray2rgb

运动模糊图像中模糊方向和模糊尺度的识别程序，用hough变换求模糊方向，用randon变换暗条纹间距求模糊尺度。(Motion blur image blur direction and blur-scale identification procedures, using hough transform for vague direction, with randon transform dark fringe spacing requirements blur scale.)

本程序应用相位一致性进行边缘检测，较以往应用灰度信息进行边缘检测算法，该算法与光照无关，且抗噪声性能强(phase of the application process consistency edge detection, gray than in the past Application information edge detection algorithm, The algorithm has nothing to do with illumination, and strong anti-noise performance)

通过手动框选目标区域，然后进行自动跟踪，使用的算法是Camshift，自适应均值漂移(By manually selecting the target area, then automatic tracking, using the algorithm is Camshift, adaptive mean shift)

直方图修正和彩色变换 功能是直方图修正和彩色变换 (Histogram correction and color conversion Function and color histogram transformation)

通过自定义数学运算，在图像处理中做直方图均衡化的matlab实现(Through custom mathematical operation, do the histogram equalization of matlab in image processing)

说明：  通过纹理方向估计消失点，通过消失点来检测道路区域。 该程序应用到了基于纹理的张量投票算法来估计消失点；通过纹理方向和颜色信息，生成用于估计两个最主要道路边界的直方图；最后将道路区域定义为两个检测到的道路边界之间且低于估计的消失点的区域(The vanishing point is estimated by the texture direction, and the road area is detected by the vanishing point. The program applies a texture-based tensor voting algorithm to estimate the vanishing point; through the texture direction and color information, generates a histogram for estimating the two most important road boundaries; finally defines the road area as two detected road boundaries. An area between and below the estimated vanishing point)

说明：  雷达发射波形。回波的模拟，以及匹配滤波。加窗控制旁瓣，以及MTD处理(Radar transmitting waveform. Echo simulation and matched filtering. Windowing control sidelobe and MTD processing)

自适应直方图均衡算法，因为是从从局部入手，所以图像增强效果显著。 (Adaptive histogram equalization algorithm, because from the start from the local image enhancement effect is remarkable.)

说明：  数字图像处理的大作业，有报告，关键代码都有注释，有一些不足，可供大家参考(Digital image processing operations, there are reports, key codes have notes, there are some shortcomings, for your reference.)

对于低水平的视觉情况下，加权核规范最小化算法及其应用。(Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision)