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说明：  《visual C++ 开发经验技术宝典》图像锐化处理，主要是将图像的亮度增加，颜色的鲜艳感增强。("Visual C++ development experience Technical Collection," Image sharpening, mainly to increase the brightness of the image, bright sense of color enhancement. )

说明：  图像去模糊，各种传统滤波算法，逆滤波、维纳滤波、LR算法等等。。。针对图像运动模糊领域(Image to blur, a variety of traditional filtering algorithms, inverse filtering, Wiener filtering, LR algorithm and so on...Aimed at the field of image motion blur)

三个很好的程序，有染色体的识别，车牌跟踪，还有神经网络设计，肯定是非常好的，希望大家从中学习(Three very good procedures, there are chromosome recognition, license plate tracking, as well as neural network design, is certainly very good, I hope you learn from)

利用稀疏表示进行图像去噪时，需要生成两种学习字典，该源码比较了生成的两种字典的优劣(Denoising sparse representation, it is necessary to generate two learning dictionary, which compares the generated source code of the merits of two dictionary)

用于Landsat5 TM数据的辐射定标(Calibration for Landsat TM data using IDL)

matlab 车牌识别，含有gui界面，边缘检测车牌定位，神经网络字符识别(Matlab license plate recognition, including GUI interface, edge detection, license plate location, neural network, character recognition)

处理图像，利用提出高频的方法，把图像中的裂纹提取出来。能比较好的把背景图像和裂纹分割开来。(Processing images, the use of the proposed high-frequency method of the cracks in the image extracted. Good background image and crack separated.)

在尽可能好的代表原始数据的前提下，PCA通过线性变换将高维空间中的样本数据投影到低维空间中。(In the best possible on behalf of the original data, under the precondition of PCA through linear transformation in the high-dimensional space sample data projection to low dimensional space)

图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得 到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点(Image inpainting is to fill the missing data in corrupted images and thus to reduce the information loss of damaged image. Traditional inpainting algorithms are dependent on specific structure of target images compressive sensing theory makes is possible to realized image inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD and MCA algorithm, which first decomposes the image into texture part and structure part, and then trains the two dictionaries for these two parts with KSVD and reconstructs the original image with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better adaptability and performance as compared with traditional algorithms.)

基于图像纹理的特征提取，使用灰度共生矩阵方法。(Texture feature extraction based on the use of gray level co-occurrence matrix method.)