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这是一个比较完整的KPCA算法，通过这个算法可以快速的找到主要的特征向量进行主成分分析。(This is a relatively complete KPCA algorithm, this algorithm can quickly find the main feature vector principal component analysis.)

图像的亮度对比度增强，可对较暗的图像进行亮度增强(The brightness of the image contrast enhancement, may be dark image brightness enhancement)

基于efficient subwindow search算法的windows实现(efficient subwindow search for windows )

读取RAW格式文件，将其转换为BMP格式文件并存储，主要实现灰度图像的转换(Read the RAW format, convert it to BMP format file and stored, mainly to achieve the conversion of grayscale images)

Zhang等人 利用相位一致性信息的特性，提出了 特征相似性方法(FSIMG) ，选取了相位一致性信息和梯度信息 作为它的两个特征，得到了较好的结果。 (A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS’ perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA s demonstrate that FSIM can achieve much higher consistency with the subjective uations than state-of-the-art IQA metrics.)

稀疏贝叶斯学习的代码，出自UCLA，大家可以下载看看(sparse bayesian learning)

说明：  首先对指纹图像进行预处理，包括图像的分割，增强，二值化，细化。对细化后的图像进行特征点的提取，最后通过细节特征匹配完成指纹的识别。(Firstly, the fingerprint image is preprocessed, including image segmentation, enhancement, binarization and thinning. The refined image is extracted by feature points, and the fingerprint recognition is completed by minutiae feature matching.)

手机软板自动检测的图像预处理及分割方法(FPC phone automatically detects the image preprocessing and segmentation methods)

对图像先加入大气湍流，再对图像进行维纳滤波(Give the image in atmospheric turbulence, and then the image is the Wiener filter)

图像反卷积 来自D. Krishnan, R. Fergus中提到的方法(image deconvolution The Matlab functions in this directory solve the deconvolution problem in the paper D. Krishnan, R. Fergus: "Fast Image Deconvolution using Hyper-Laplacian Priors", Proceedings of NIPS 2009. )