Lightweight Image Super-Resolution with Multi-Residual Feature Aggregation Network

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Lightweight Image Super-Resolution with Multi-Residual Feature Aggregation Network 15 Pages Posted: 3 Nov 2022 See all articles by Jiayi QinJiayi QinSichuan UniversityXiaomin YangSichuan University Abstract Recently, single image super-resolution (SISR) methods using deep convolution neural networks (CNNs) have achieved remarkable performance. Especially, lightweight networks have received unprecedented attention because of their broad application prospects. However, existing methods for lightweight SR lack the adequate utilization of hierarchical features, which weakens the representation ability of the network. To alleviate this issue, we propose an effective and accurate multi-residual feature aggregation network (MrFAN) for SISR. Specifically, we design a multi-residual block (MRB) to boost the representation ability of the network. By adopting the multi-residual learning (MRL) strategy, MRB can efficiently improve reconstruction results with slight computational resources, compared with the ordinary residual block (RB). To use the hierarchical features sufficiently, we construct a multi-residual aggregation block (MRAB) with a multi-residual feature aggregation (MRFA) strategy. Utilizing the MRFA, informative high-frequency features, extracted by the hierarchical feature fusion (HRFF), are refined through hierarchical feature aggregation (HFA) at deep layers. Finally, we build our MrFAN by densely stacking MRABs and introducing double-residual learning (DRL) strategy into the network at the global level. Extensive experiments demonstrate that the MrFAN is superior to the state-of-the-art SISR models while taking up less computing resources. Keywords: Single image super-resolution, convolution neural network, lightweight network, multi-residual feature aggregation Suggested Citation: Suggested Citation Qin, Jiayi and Yang, Xiaomin, Lightweight Image Super-Resolution with Multi-Residual Feature Aggregation Network. Available at SSRN: https://ssrn.com/abstract=4266675 Jiayi Qin Sichuan University ( email ) No. 24 South Section1, Yihuan Road,Chengdu, 610064China Xiaomin Yang (Contact Author) Sichuan University ( email ) No. 24 South Section1, Yihuan Road,Chengdu, 610064China Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 3 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo-4dc in 0.218 seconds