BM3D Image Denoising with Shape-Adaptive Principal Component Analysis
BM3D Image Denoising with Shape-Adaptive Principal Component Analysis
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发表时间：
2009-04
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通讯作者：
Kostadin Dabov;A. Foi;V. Katkovnik;K. Egiazarian
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作者：
Kostadin Dabov;A. Foi;V. Katkovnik;K. Egiazarian
我们提出了一种利用非局部图像建模、主成分分析（PCA）和局部形状自适应各向异性估计的图像去噪方法。通过将相似的图像块分组成三维组，利用非局部建模。降噪是通过在这些组上应用三维变换的频谱收缩来实现的。收缩的有效性取决于变换稀疏表示真实图像数据的能力，从而将其从噪声中分离出来。我们建议从两个方面改进稀疏性。首先，我们使用具有数据自适应形状的图像补丁（邻域）。其次，我们将这些自适应形状的邻域作为三维变换的一部分，提出PCA。主成分基是通过对经验二阶矩矩阵的特征值分解得到的，这些经验二阶矩矩阵是由相似自适应形状的邻域群估计出来的。我们的研究表明，该方法具有竞争力，并且优于目前一些最好的去噪方法，特别是在保留图像细节和引入很少的伪影方面。
We propose an image denoising method that ex- ploits nonlocal image modeling, principal component analysis (PCA), and local shape-adaptive anisotropic estimation. The nonlocal modeling is exploited by grouping similar image patches in 3-D groups. The denoising is performed by shrinkage of the spectrum of a 3-D transform applied on such groups. The effectiveness of the shrinkage depends on the ability of the transform to sparsely represent the true-image data, thus separating it from the noise. We propose to improve the sparsity in two aspects. First, we employ image patches (neighborhoods) which can have data-adaptive shape. Second, we propose PCA on these adaptive-shape neighborhoods as part of the employed 3-D transform. The PCA bases are obtained by eigenvalue decompo- sition of empirical second-moment matrices that are estimated from groups of similar adaptive-shape neighborhoods. We show that the proposed method is competitive and outperforms some of the current best denoising methods, especially in preserving image details and introducing very few artifacts.