test

基于彩色信息的道路图象分割,可以有效的去除道路上的阴影.(Based on color information of the road image segmentation, can effectively remove the shadow on the road.)

加权核范数最小化应用于图像处理的代码，很实用，且是图像处理领域很新的方法源码，所以该方法效果很好，希望对大家有用。(Nuclear weighted norm minimization codes used in image processing, it is useful, and it is a new image processing method for the source, so that this method works well, want to be useful.)

基于自适应技术的遗传算法matlab源码，可应用于图像的去噪处理(based on adaptive technology GA Matlab source can be applied to the image denoising)

基于LEE滤波的极化SAR去噪 matlab实现(jiyu lee lvbo jihua SAR matlab)

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。(simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.)

有效地MR图像重建算法，主要用于在MR图像的成像过程中对图像进行压缩(Efficient MR Image Reconstruction for Compressed MR Imaging)

基于图像处理，vc++平台的水果识别。通过亮度调整，梯度处理，边缘图像，边界跟踪，计算颜色形状等，计算出结果。(Based on image processing, fruit recognition vc++ platform. Through the brightness adjustment, gradient, edge image, boundary tracking, calculation of color shape, the calculated results. )

NEDI源代码，是用于超分辨率重建的，并附上原文献，希望对大家有用~(The NEDI source code for super-resolution reconstruction, together with Uehara literature, we want to ~)

图像处理课程设计——《人脸识别》，用matlab实现的，代码加实现，以及设计报告(Image processing designation of face recognize.)

说明：  八值图像连通区域标记，为每个连通区域分配一个唯一的标号，处理后的图像按照从左到右，从上到下的顺序获得连续的标号(Binary image connected region eight marks, for each connected region a unique label distribution deal with in accordance with the image from left to right, from top to bottom order of access to continuous labeling)