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db4小波的提升小波分解，从已发表的论文中得来，可放心使用(db4 wavelet lifting wavelet decomposition, the published papers come, you can be assured)

将RGB格式图像转换到YUV空间，输入RGB图像返回YUV格式数据(Converting RGB format images to YUV space)

一种有效的全景图拼合预处理算法.适用于图像的拼接。是论文！(An effective pre-processing algorithm together panorama. Splicing applied to images. Is a thesis!)

用于实现高密度人群聚合检测 包含：边缘检测、灰度共生矩阵计算、SVM特征分量的分类(For achieving high-density population polymerization detector comprises: edge detection, gray level co-occurrence matrix calculation, classification SVM characteristic component)

提出了一种基于非下采样Contourlet 变换的自适应图像去噪方法 首先对噪声图像进行非下采样Contourlet 变换，得到各 个尺度各个方向子带的系数，再根据该系数的能量自适应地调整去噪阈值 实验表明，与Contourlet 多尺度阈值去噪 Contourlet 自 适应阈值去噪相比，该方法在保留图像边缘细节的同时，能提高图像的PSNR 值，减少了Gibbs 现象(A new adaptive method of image de-noising based on NonSubsampled Contourlet Transform （NSCT）is presented.Firstly， the noisy image is decomposed into a set of multiscale and multidirectional frequency subbands by NSCT， and then the threshold is adapted automatically according to the energy of subband coefficients.Compared with the multi-scale threshold using contourlet transform and using adaptive threshold based on contourlet transform， the simulation results show that the performance of this method is superior in PSNR， meanwhile it can maintain the image edge and reduce the Gibbs phenomen)

无监督分类方法，包括kmeans，som和fcm，非常好用(Unsupervised classification methods, including kmeans, som and fcm, very easy to use)

有限脊波变化matlab源码 基本的变换(Limited changes in ridgelet basic transform matlab source)

二维图像视觉显著性检测，效果非常好。California Institute of Technology的 Jonathan Harel写的，很经典。(Two-dimensional image saliency detection, the effect was very good. California Institute of Technology' s Jonathan Harel written, very classic.)

MATLAB编写的增强图像分辨率的程序，可以提高图像分辨率，使图像更加清晰。(Written MATLAB program enhanced image resolution, image resolution can be improved to make the image clearer.)

说明：  能够实现对生物芯片的识别和图像处理，识别有效芯片区域，读取阴性和阳性点分布和数量。(It can realize the recognition and image processing of biochip, identify the effective chip area, read the distribution and number of negative and positive points.)