demo

该算法首先通过字典学习得到含噪图像的冗余字典，然后对相似的图像块进行聚类构成块群，并通过迭代收缩和L1正则化约束，对同类的图像块在字典上进行稀疏表示，以达到降噪的目的。实验结果表明，在常规的图像处理上，本文提出的算法能较好的保留图像的结构信息，与K-SVD和BM3D等现有的流行算法相比，具有更高的峰值信噪比（PSNR）(It firstly get the redundant dictionary of a noised image by dictionary learning.Then,the image patches are gathered according to their similarities.Meanwhile,the similar patches get sparse representation showed in dictionaries by iterative shrinkage and L1 regularization constraints and eventually the image is restored and noise is removed.The experimental results indicate that the proposed algorithm can well preserve the structure information of the common image with a higher Peak Signal to Noise Ratio(PNSR),compared with state-of-the-art algorithms,such as K-SVD and BM3D)

图像的纹理特征参数的灰度共生矩阵的计算，该方法效果好，速度快，易理解。(The image texture characteristic parameters GLCM calculation, the method effective, fast, easy to understand.)

vssamp算法，图像变步长自动匹配重构重构算法，基于samp算法，更快速有效的重构复杂图像(vssamp variable step size image reconstruction algorithm automatically matches reconstruction, samp algorithm based on more quickly and efficiently reconstruct complex images)

说明：  IQA评估指标包括PLCC，SROCC，KROCC，PSNR，SSIM (Structural Similarity），MSE (Mean Square Error)，RMSE (Root Mean Square Error) ，信息熵 (Information Entropy)算法(IQA evaluation indicators include PLCC, SROCC, KROCC, PSNR, SSIM (Structural Similarity), MSE (Mean Square Error), RMSE (Root Mean Square Error) , Information Entropy (Information Entropy) algorithm)

圆心检测算法，使用matlab根据sci文章仿写，在图像质量不高的情况下检测效率较高。(Center of a circle detection algorithm, the use of matlab under the sci article仿写, image quality is not high in the case of a higher detection efficiency.)

斐波那契数的实现算法及分析,标准化实验报告样本()

利用K-高斯混合模型提取视频的前景信息。(The use of K-Gaussian mixture model for the future of video information extraction)

对SAR图像分割 用马尔科夫随机长进行的。可以任意输入类别数目(For SAR image segmentation using Markov old airport)

基于熵率的超像素分割，图像处理，图像分割技术(superpixel segmentation based on entropy rate, image processing, image segmentation technology)

KMEANS代码段，用于图像分割，已配置完成(KMEANS,use to achieve image segmentation)