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Wangmeng Zuo
Researcher at Harbin Institute of Technology
Publications - 570
Citations - 46901
Wangmeng Zuo is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 80, co-authored 496 publications receiving 28053 citations. Previous affiliations of Wangmeng Zuo include SenseTime & Intel.
Papers
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Journal ArticleDOI
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
TL;DR: Zhang et al. as mentioned in this paper proposed a feed-forward denoising convolutional neural networks (DnCNNs) to handle Gaussian denobling with unknown noise level.
Proceedings ArticleDOI
Weighted Nuclear Norm Minimization with Application to Image Denoising
TL;DR: Experimental results clearly show that the proposed WNNM algorithm outperforms many state-of-the-art denoising algorithms such as BM3D in terms of both quantitative measure and visual perception quality.
Journal ArticleDOI
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
TL;DR: Zhang et al. as discussed by the authors proposed a denoising convolutional neural network (DnCNN) to handle Gaussian denoizing with unknown noise level, which implicitly removes the latent clean image in the hidden layers.
Journal ArticleDOI
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
Kai Zhang,Wangmeng Zuo,Lei Zhang +2 more
TL;DR: FFDNet as discussed by the authors proposes a fast and flexible denoising convolutional neural network with a tunable noise level map as the input, which can handle a wide range of noise levels effectively with a single network.
Proceedings ArticleDOI
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
TL;DR: The Efficient Channel Attention (ECA) module as discussed by the authors proposes a local cross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution, which only involves a handful of parameters while bringing clear performance gain.