Weighted Nuclear Norm Minimization with Application to Image Denoising

By Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng. 2014

2,138 citations recorded by the source corpus.

Subfields: Computational Mechanics, 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): “6240 WNNM [9] 27.”
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (2016, methodology): “First, those methods generally involve a complex optimization problem in the testing stage, making the denoising process time-consuming [6], [13].”
Kindling the Darkness: A Practical Low-light Image Enhancer (2019, background): “The most popular schemes arguably go to BM3D [22] and WNNM [24].”
Beyond Brightening Low-light Images (2021, background): “Classic ones model/regularize the problem by utilizing some speciﬁc priors of natural clean images, like non-local self-similarity, piecewise smoothness, signal (representation)sparsity, etc. Themostpopularschemesarguably go to BM3D (Dabov et al. 2007) and WNNM (Gu et al. 2014).”
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections (2016, methodology): “For existing methods, their denoising performance does not differ much, while our model achieves 0.38dB, 0.47dB, 0.49dB and 0.42dB higher of PSNR over WNNM. Table 2: Average PSNR”
MemNet: A Persistent Memory Network for Image Restoration (2017, methodology): “For qualitative comparisons, we use public codes of PCLR [2], PGPD [37] and WNNM [9].”
Variational Denoising Network: Toward Blind Noise Modeling and Removal (2019, methodology): “Comparison methods: Several state-of-the-art denoising methods are adopted for performance comparison, including CBM3D [11], WNNM [16], NCSR [14], MLP [10], DnCNN-B [44], Mem-Net [38], FFDNet [45], UDNet [24] and CBDNet [17].”
Methods for image denoising using convolutional neural network: a review (2021, positive): “The convolution separation block decomposed the input noise into sub-blocks [66, 67].”
Rank Minimization for Snapshot Compressive Imaging (2018, methodology): “There are some heuristic approaches on how to determine s(2)n based on current estimations [24].”
Non-local Color Image Denoising with Convolutional Neural Networks (2016, methodology): “) BM3D [7] LSSC [25] EPLL [41] WNNM [15] CSF(5)7×7 [34] TNRD(5)7×7 [6] DGCRF8 [37] MLP [5] NLNet(5)5×5 NLNet(5)7×7 15 31.”
Snapshot Compressive Imaging: Theory, Algorithms, and Applications (2021, methodology): “Advanced denoising algorithms, such as weighted nuclear norm minimization (WNNM) [84], have also been used [70].”
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images (2021, methodology): “Traditional image denoising methods such as BM3D [7], NLM [4], and WNNM [11], use local or non-local structures of an input noisy image.”
The Little Engine That Could: Regularization by Denoising (RED) (2016, positive): “…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]).”
Attention-guided CNN for image denoising (2020, methodology): “[13] extracted more information under different conditions as weights to address the nuclear norm minimization problem for image denoising.”
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising (2017, methodology): “The methods in [19, 28] ignore the non-local self-similarity of natural images [1, 3].”
A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “[172] studied a weighted nuclear norm minimization (WNNM) method withF -norm fidelity under different weighting rules optimized by non-l ocal self-similarity for image denoising.”
Multi-stage image denoising with the wavelet transform (2022, methodology): “Specifically, quantitative analysis is evaluated by comparing PSNR, feature similarity index measure (FSIM)[37], structural similarity index measure (SSIM)[31], learned perceptual image patch similarity (LPIPS) as well as DeepFeatures[38] and Inception-Score[25] of popular denoising methods containi”