Dictionary-Based Image Denoising by Fused-Lasso Atom Selection
Dictionary-Based Image Denoising by Fused-Lasso Atom Selection
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DOI：
10.1155/2014/368602
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发表时间：
2014-08
影响因子：
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通讯作者：
Ao Li;Hayaru Shouno
中科院分区：
文献类型：
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作者：
Ao Li;Hayaru Shouno
我们提出了一种通过融合套索和字典学习的有效图像去噪方案。该计划有两个重要贡献。第一个是我们通过主成分分析（PCA）将图像聚类成许多子集来学习基于块的自适应字典，这可以更好地保留局部几何结构。第二个是我们通过融合套索和聚类学习字典对每个子集中的补丁进行编码，并提出了迭代 Split Bregman 来快速解决它。我们通过几个实验来展示这些功能。结果表明，该方案与一些优秀的去噪算法相比具有竞争力。
We proposed an efficient image denoising scheme by fused lasso with dictionary learning. The scheme has two important contributions. The first one is that we learned the patch-based adaptive dictionary by principal component analysis (PCA) with clustering the image into many subsets, which can better preserve the local geometric structure. The second one is that we coded the patches in each subset by fused lasso with the clustering learned dictionary and proposed an iterative Split Bregman to solve it rapidly. We present the capabilities with several experiments. The results show that the proposed scheme is competitive to some excellent denoising algorithms.