文件名称:CSR_Denoising
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- 上传时间：2017-05-02
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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
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CSR_Denoising
.............\Data
.............\....\images
.............\....\......\barbara.tif
.............\....\......\boat.tif
.............\....\......\cameraman.tif
.............\....\......\couple.tif
.............\....\......\fingerprint.tif
.............\....\......\hill.tif
.............\....\......\house.tif
.............\....\......\Lena512.tif
.............\....\......\man.tif
.............\....\......\Monarch_full.tif
.............\....\......\peppers256.tif
.............\....\......\straw.tif
.............\....\......\textures
.............\....\......\........\hex.tif
.............\Denoising_Main.m
.............\Results
.............\.......\Denoising_results
.............\.......\.................\nsig_20
.............\.......\.................\.......\CSR_de_PSNR_SSIM.txt
.............\.......\.................\.......\PCA_CSR_PSNR_SSIM.txt
.............\Utilities
.............\.........\Add_noise.m
.............\.........\cal_ssim.m
.............\.........\Clustering_PCA_New.m
.............\.........\csnr.m
.............\.........\CSR_Denoising.m
.............\.........\CSR_Thresholding.m
.............\.........\ext_im.m
.............\.........\find_blks.m
.............\.........\find_blks_fast.m
.............\.........\find_blks_fast2.m
.............\.........\getpca.m
.............\.........\Image_Denoising.m
.............\.........\Proc_cls_idx.m
.............\.........\Set_PCA_idx.m
.............\.........\soft.m
.............\.........\Soft_PCA.m
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