fenkuai1

利用低通滤波器对图像进行插值放大：按放大的倍数对原图像进行插零，插零后的图片与低通滤波函数进行卷积，原先插入的零的位置自动替换为适当的插值，如此图像就已经插值放大了，图像质量还不错。(Double the number of pixels of each row and column by adding a zero value pixel between each pixel of the image.Then operate with two kinds of low-pass filters to get the enlarged image.)

实现非负ICA的盲源分离，可用于对图像等非负混合信号的分离(Non-negative ICA blind source separation, can be used for the separation of mixed-signal images and other non-negative)

PCA_进行indian_pines数据集合分类。((use PCA to classify the indian_pines data with train and test data in the rar))

形态学pathopenings的实现，对线特征提取等图像处理有令人惊奇的效果。(Morphological pathopenings the realization of the line feature extraction and other image processing have surprising effects.)

说明：  EM算法是最为经典的统计图像分割方法，代码基于matlab，使用EM算法实现图像分割,源代码加实例图像，非常好用(By using EM algrithm to realize the segementation of image)

用于计算两幅图片之间的峰值信噪比，从而衡量图片重建质量(It used to calculate the peak signal to noise ratio between the two pictures, which measure the quality of the reconstructed image)

用FCM 对自然图像 sar图像以及纹理图像进行快速分割 ，用一维和二维直方图分别对图像进行特征映射 从而快速的对图像进行分割 包含结果截图 (By FCM sar image of natural images quickly and texture image segmentation, with one-dimensional and two-dimensional histogram of the image feature maps were so fast image segmentation screenshot contains the results)

一个医学图像配准的源程序，希望对大家有用(A medical image registration of the source, in the hope that useful to everybody)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SL0算法，对256*256的lena图处理，比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 (Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and SL0 algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SL0 algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

Matlab写的SVM图像分类程序，是完整的可运行的图像分类程序，高手可以研究一下。(Matlab write SVM image classification procedure is complete working image classification procedures, experts can look at.)