Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

By Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, Karen O. Egiazarian. 2007

9,075 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising (2019, background): “BM3D [4] 27.”
Deep Image Prior (2017, methodology): “For reference, the result of a state-of-the-art nonlearned denoising approach [6] is shown.”
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (2016, methodology): “, blind Gaussian denoising), DnCNN with a single model can still outperform BM3D [2] and TNRD [16] trained”
Relation-Aware Global Attention for Person Re-Identification (2019, background): “Exploration of non-local/global information has been demonstrated to be very useful for image denoising [3, 8, 4], texture synthesis [10], super-resolution [14], inpainting [2], and even high level tasks such as image recognition, object segmentation [35] and action localization [7].”
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (2020, methodology): “In these methods, the off-line denoising operation [5] is applied to suppress noise, which sometimes also leads to detail blurriness.”
Foundations and Advances in Deep Learning (2014, methodology): “The performance of these neural networks was shown to be comparable to, or sometimes better than, conventional methods of image denoising such as BM3D (Dabov et al., 2007) or K-SVD (Portilla et al.”
Nonlocally Centralized Sparse Representation for Image Restoration (2013, methodology): “…denoising, deblurring and super-resolution, demonstrate that the proposed NCSR based IR method can achieve highly competitive performance to state-of-the-art denoising methods (e.g., BM3D [17], [39]–[41], LSSC [18]), and outperforms state-of-the-art image deblurring and super-resolution methods.”
Learning Enriched Features for Real Image Restoration and Enhancement (2020, methodology): “Method DnCNN MLP GLIDE TNRD FoE BM3D WNNM NLM KSVD EPLL CBDNet RIDNet VDN MIRNet [120] [11] [96] [20] [85] [21] [38] [10] [2] [128] [39] [5] [118] (Ours)”
Compressive Sensing via Nonlocal Low-Rank Regularization (2014, methodology): “The multimedia package contains the “CS Toolbox via Nonlocal Low-rank Regularization,” which is a Matlab implementation of the algorithm described in the accompanying paper titled “Compressive Sensing via Nonlocal Low-rank Regularization.””
Image De-Raining Transformer (2022, methodology): “On the other hand, our work primarily focuses on establishing a transformer-based method for single image de-TABLE [114] Model BM3D[115] DnCNN[38] VDN[116] DANet[117] CycleISP[118] MIRNet[119] DeamNet[120] MPRNet[79] DAGL[121] Uformer-S raining.”
Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery (2018, neutral): “The comparison methods include: band-wise K-SVD [1] 3 and band-wise BM3D [13], 4 representing state-of-the-arts for 2D extended band-wise meth-ods; 3D-cube K-SVD [18], 5 ANLM3D [46] 6 and BM4D [44], [45], 7 representing state-of-the-arts for 2D extended 3D-cube-based methods; LRTA [49], PARAFAC…”