Patch-Based Near-Optimal Image Denoising

By Priyam Chatterjee, Peyman Milanfar. IEEE Transactions on Image Processing, 2012

328 citations recorded by the source corpus.

How researchers see this paper

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The Little Engine That Could: Regularization by Denoising (RED) (2016, methodology): “…on the availability of algorithms proposed in the past decade that can treat this task extremely eﬀectively and stably, getting very impressive results, which also tend to be quite concentrated (see, for example, the work reported in [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 42]).”
Weighted Schatten $p$ -Norm Minimization for Image Denoising and Background Subtraction (2015, methodology): “Firstly, we test the performance of the proposed WSNM in image denoising, and compare it with six representative algorithms: block-matching 3D filtering [6] (BM3D), patch-based near-optimal image denoising [37] (PBNO), spatially adaptive”
Sparse Modeling for Image and Vision Processing (2014, methodology): “[2009], Plow refers to Chatterjee and Milanfar [2012], EPLL to Zoran and Weiss [2011], CSR to Dong et al.”
A Nonlocal Bayesian Image Denoising Algorithm (2013, methodology): “1 a local model is estimated in a neighborhood of each patch, in the PLOW [9] method the idea is to learn from the image a sufficient number of patch clusters, actually 15, and to apply the LMMSE estimate to each patch after having assigned it to one of the clusters obtained by clustering.”
Poisson Noise Reduction with Non-local PCA (2012, background): “the clustering is directly performed in the patch domain similarly to [9].”