稀疏分解图像去噪

上传者: 24599599 | 上传时间: 2019-12-21 20:35:14 | 文件大小: 2.07MB | 文件类型: zip
传统的去噪方法往往假设含噪图像的有用信息处在低频区域,而噪声信息处在高频区域,从而基于中值滤波、Wiener 滤波、小波变换等方法实现图像去噪,而实际上这种假设并不总是成立的。基于图像的稀疏表示,近几年来研究者们提出了基于过完备字典稀疏表示的图像去噪模型,其基本原理是将图像的稀疏表示作为有用信息,将逼近残差视为噪声。利用 K-SVD 算法求得基于稀疏和冗余的训练字典,同时针对 K-SVD 算法仅适合处理小规模数据的局限,通过定义全局最优来强制图像局部块的稀疏性。文献[28]提出了稀疏性正则化的图像泊松去噪算法,该算法采用 log 的泊松似然函数作为保真项,用图像在冗余字典下稀疏性约束作为正则项,从而取得更好的去噪效果。

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  • 明道士 :
    可以运行的
    2019-05-09

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