Two-stage image denoising by principal component analysis with local pixel grouping

By Lei Zhang, Weisheng Dong, David Dapeng Zhang, Guangming Shi. Pattern Recognition, 2010

761 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/a7bf2789b849fcdaa37ed998d5af945167025600

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms (2014, methodology): “In this paper, wavelet-based denoising methods are discussed as an instance due to its popularity [28], [35]–[37].”
Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling (2013, methodology): “Such kind of nonlocal similarity is very helpful to improve the image restoration output, and it has been successfully used in image denoising [31-34], deblurring [35-36] and super-resolution [37-38, 49].”
Poisson Noise Reduction with Non-local PCA (2012, methodology): “Our work is inspired by recent methods combining PCA with patch-based approaches [15, 33, 47] for the Additive White Gaussian Noise (AWGN) model, with natural extensions to spectral imaging [13].”
A High-Quality Denoising Dataset for Smartphone Cameras (2018, methodology): “From the PSNR results, we can see that classic patch-based and optimization-based methods (e.g., BM3D, KSVD, LPGPCA, and WNNM) are outperforming learning-based methods (e.g., MLP, TNRD, and DnCNN) when tested on real images.”
A Nonlocal Bayesian Image Denoising Algorithm (2013, methodology): “These oracle strategies are conceptually different from a simple iteration such as the one used in TSID [47].”
A Modified Adaptive PCA Learning based Method for Image Denoising (2013, methodology): “Compared with WT that uses a fixed basis function to decompose the image, the proposed LPG-PCA method is a spatially adaptive image representation so that it can better characterize the image local structures [5].”
Group-Based Sparse Representation for Image Restoration (2014, methodology): “Similar to the idea of dictionary learning strategy using similar patches in [3], we propose to learn the adaptive dictionary k G D for each group k G x directly from its estimate k G r , because in practice the original image x is not available for learning all the groups’ dictionaries { } k G D .”
Centralized sparse representation for image restoration (2011, methodology): “Such a local PCA strategy has been used in [28, 8, 15, 14] for image reconstruction.”
Diffusion Weighted Image Denoising Using Overcomplete Local PCA (2013, background): “…over small local windows instead of the whole image what significantly produces sparser representation (in the extreme case, i.e. in homogeneous areas, it can result in the sparsest representation not requiring any component to represent the data but on only the mean value of the region).”
Image Denoising Using the Higher Order Singular Value Decomposition (2013, neutral): “There also exist papers such as [21] and [43] which infer spatially varying orthonormal bases at each pixel using PCA.”
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, methodology): “promising results in various IR tasks, especially in image denoising [15], [36], [39].”
A Review Paper: Noise Models in Digital Image Processing (2015, background): “Figure 6 Lena image [20] of Speckle noise with variance 0.04”