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Collaborative filtering denoising algorithm based on the nonlocal centralized sparse representation model

机译:基于非局部集中式稀疏表示模型的协同过滤去噪算法

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An improved image denoising algorithm based on block-matching and 3D collaborative filtering (BM3D) is proposed in this manuscript. Instead of using the same filtering model for all patches in an image, we employ two different nonlocal filtering models in edge and smooth regions, respectively. We realize it by using the nonlocal centralized sparse representation (NCSR) to capture both local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks. Experimental results demonstrate that the proposed method outperforms several state-of-the-art denoising methods in terms of objective metrics and visual quality.
机译:本文提出了一种基于块匹配和3D协同滤波(BM3D)的改进图像去噪算法。我们没有对图像中的所有色块使用相同的过滤模型,而是分别在边缘和平滑区域中采用了两种不同的非局部过滤模型。我们通过使用非局部集中式稀疏表示(NCSR)捕获小波系数的局部稀疏性和分组块的非局部相似性来实现它。实验结果表明,在客观指标和视觉质量方面,该方法优于几种最新的去噪方法。

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