L1 model-driven recursive multi-scale denoising network for image super-resolution

Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to the huge scale of the training data. However, it will bring a huge amount of weight...

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Vydané v:	Knowledge-based systems Ročník 225; s. 1
Hlavní autori:	Sun, Zhongfan, Zhao, Jianwei, Zhou, Zhenghua, Gao, Qingqing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 05.08.2021 Elsevier Science Ltd
Predmet:	[formula omitted] model-driven Algorithms Data Deep learning Denoising Denoising network Fidelity Grammatical aspect Image resolution Iteration algorithm Iterative algorithms Iterative methods Machine learning Majorization–minimization algorithm Minimization Networks Noise reduction Optimization Outliers (statistics) Prior knowledge Recursion Soft thresholding operator Super-resolution Deep learning Denoising network Super-resolution Iteration algorithm ℓ1 model-driven Majorization–minimization algorithm Soft thresholding operator
ISSN:	0950-7051, 1872-7409
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