houghtuoyuan3

说明：  色彩转移法为灰度图像上色。实现paper“Transferring color to grayscale image”, 通过纹理匹配，找出灰度图像与彩色图像之间的匹配区域，在对应区域进行颜色转移。压缩包内附函数说明。(Color to grayscale color transfer method. To achieve paper &quot Transferring color to grayscale image&quot , by texture matching, gray images and color images to find the matching between the regions, the color shift in the corresponding area. Archive containing the function description.)

说明：  图像滤波，中值滤波，自适应，对比结果，图像去燥使用(Image filtering, median filtering, adaptive, comparative results, medical image use)

图形图像感兴趣区域提取的方法，个人总结的~可以直接运行！(Graphic method of region of interest extraction, can be directly run personal summary!)

采用最小交叉熵，最大熵，OTSU阈值分割(Using minimum cross entropy, maximum entropy, OTSU thresholding)

自适应光学仿真软件，希望对大家由帮助！我还没有弄明白原理，希望一起学习(Adaptive optics simulation software, I hope all of you by the help! I have not understood the principle, I hope to learn)

总变分正则化超分辨率重建 包括两种范数形式的正则化(Total Variation Regularized super-resolution reconstruction, including two-norm in the form of regularization)

对李纯明的DRLSE进行了改进 利用各向异性扩散提高了分割弱边缘的能力 改进的高斯滤波代替惩罚项 加快了演化速度 节省了时间(The DRLSE to LiChunMing improved using anisotropic diffusion improve the segmentation of improvement of the ability weak edge gaussian filtering instead of penalty term accelerated evolution speed save time)

遗传算法/大津法/区域生长法/迭代法分割图像,有详细注释，适合学习(Genetic algorithm/dajin method/area growth method/iteration method image segmentation, and have the detailed notes, suitable for learning )

说明：  INSAR相位解缠算法，使用的python语言，枝切法进行相位解缠。(INSAR phase unwrapping algorithm, using python language, branch cut method for phase unwrapping.)

基于图像检索的matlab代码，基于内容的图像检索中的一些关键环节：特征提取：颜色直方图；纹理特征等 相似度：马氏距离，欧氏距离等 相关反馈：机器学习方法，如SVM，神经网络等 检索与分类：两个很相似的样本距离很小，虽然两个不相似的样本距离未必很大(content-based image retrieval of some of the key issues : Feature Extraction : color histogram Texture characteristics of similarity : Mahalanobis distance, the Euclidean distance relevance feedback : machine learning methods, such as SVM. Neural network search and classification : two very similar samples from the small, although the two are similar to the samples may not be much distance)