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Two-stream deep sparse network for accurate and efficient image restoration

文献类型：期刊论文

作者Wang, Shuhui1; Hu, Ling1,2; Li, Liang1; Zhang, Weigang3; Huang, Qingming1,2,4
刊名COMPUTER VISION AND IMAGE UNDERSTANDING
出版日期2020-11-01
卷号200页码:11
关键词Two-stream sparse network Image restoration Image super-resolution Image denoising
ISSN号1077-3142
DOI10.1016/j.cviu.2020.103029
英文摘要Deep convolutional neural network (CNN) has achieved great success in image restoration. However, previous methods ignore the complementarity between low-level and high-level features, thereby leading to limited image reconstruction quality. In this paper, we propose a two-stream sparse network (TSSN) to explicitly learn shallow and deep features to enforce their respective contribution to image restoration. The shallow stream learns shallow features (e.g., texture edges), and the deep stream learns deep features (e.g., salient semantics). In each stream, sparse residual block (SRB) is proposed to efficiently aggregate hierarchical features by constructing sparse connections among layers in the local block. Spatial-wise and channel-wise attention are used to fuse the shallow and deep stream which recalibrates features by weight assignment in both spatial and channel dimensions. A novel loss function called Softmax-L-1 loss is proposed to increase penalties of pixels that have large L-1 loss (i.e., hard pixels). TSSN is evaluated with three representative IR applications, i.e., single image super-resolution, image denoising and JPEG deblocking. Extensive experiments demonstrate that TSSN outperforms most of state-of-the-art methods on benchmark datasets on both quantitative metric and visual quality.
资助项目National Key R&D Program of China[2018AAA0102003] ; National Natural Science Foundation of China[61672497] ; National Natural Science Foundation of China[61771457] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61931008] ; Key Research Program of Frontier Sciences of CAS[QYZDJ-SSW-SYS013]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000579188100001
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
源URL[http://119.78.100.204/handle/2XEOYT63/15708]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Liang
作者单位1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
3.Harbin Inst Technol, Weihai 264200, Peoples R China
4.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Shuhui,Hu, Ling,Li, Liang,et al. Two-stream deep sparse network for accurate and efficient image restoration[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2020,200:11.
APA Wang, Shuhui,Hu, Ling,Li, Liang,Zhang, Weigang,&Huang, Qingming.(2020).Two-stream deep sparse network for accurate and efficient image restoration.COMPUTER VISION AND IMAGE UNDERSTANDING,200,11.
MLA Wang, Shuhui,et al."Two-stream deep sparse network for accurate and efficient image restoration".COMPUTER VISION AND IMAGE UNDERSTANDING 200(2020):11.

入库方式： OAI收割

来源：计算技术研究所

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