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Sparse-Representation-

于 2017-05-16 发布文件大小:2659KB

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稀疏表示的毕业参考论文，在图像去噪、图像修复、人脸识别及压缩感知等图像处理领域中的应用进行了总结.(Sparse representation of graduation reference papers, summarized in the image denoising, image restoration, face recognition and compressed sensing and other image processing applications)

文件列表：

稀疏表示
........\图像稀疏表示及其在图像处理中的应用_孙君顶.pdf,218143,2016-05-16
........\图像过完备稀疏表示理论及应用综述_陈垚佳.pdf,264107,2016-05-16
........\基于图像块分类稀疏表示的超分辨率重构算法_练秋生.pdf,466491,2016-05-15
........\基于形态成分稀疏表示的联合变换图像去噪_赵秀娟.pdf,521096,2016-05-16
........\基于稀疏表示的小麦品种识别_冯丽娟.pdf,334250,2016-05-16
........\基于类内稀疏表示的人脸识别_余庆辉.pdf,303094,2016-05-16
........\改进匹配追踪算法及其在图像压缩中的应用_陆望.pdf,1190882,2016-05-15

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workspace03_PSNR
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均值便宜跟踪的示例，预报误差法参数辨识-松弛的思想，用于信号特征提取、信号消噪。( Example tracking mean cheap, Prediction Error Method for Parameter Identification- the idea of relaxation, For feature extraction, signal de-noising.)

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说明：将物体用一堆点框出来，实际先初始化一堆点，然后迭代靠近物体边缘最终框出物体。(The object is framed with a pile of points. In fact, first initialize a pile of points, then iterate close to the edge of the object, and finally frame the object.)

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