Udaformer: Underwater Image Enhancement Based on Dual Attention Transformer

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 Udaformer: Underwater Image Enhancement Based on Dual Attention Transformer 12 Pages Posted: 14 Jul 2022 See all articles by Zhen ShenZhen ShenNingbo UniversityHaiyong XuNingbo UniversityTing LuoNingbo UniversityYang SongNingbo University of TechnologyZhouyan HeNingbo University of Technology There are 2 versions of this paper Udaformer: Underwater Image Enhancement Based on Dual Attention Transformer Number of pages: 12 Posted: 14 Jul 2022 You are currently viewing this paper Udaformer: Underwater Image Enhancement Based on Dual Attention Transformer Number of pages: 12 Posted: 14 Jul 2022 Abstract Underwater images suffer from seriously degraded due to wavelength-dependent light scatter and abortion of the underwater environment, which impacts the application of high-level computer vision tasks. Considering the characteristics of uneven degradation and loss of color channel of underwater images, a novel underwater image enhancement method, namely UDAformer, based on Dual Attention Transformer Block (DATB), including Channel Self-Attention Transformer (CSAT) and Shifted Window Pixel Self-Attention Transformer (SW-PSAT), is proposed. Specifically, the underwater image enhancement based on channel self-attention alone is not necessarily sufficient due to the severe and uneven degradation of underwater images. Therefore, a novel fusion method combining channel and pixel self-attention is proposed for efficient encoding and decoding of underwater image features. Then, the shifted window method for the pixel self-attention is proposed to improve computational efficiency. Further, convolution is constructed to calculate the self-attention weight matrix so that the proposed UDAformer can flexibly process input images of various resolutions and reduce network parameters. Finally, the underwater images are recovered through the design of skip connections based on the underwater imaging model. Experimental results demonstrate the proposed UDAformer surpasses previous state-of-the-art methods, both qualitatively and quantitatively. Keywords: underwater image enhancement, self-attention mechanism, Transformer Suggested Citation: Suggested Citation Shen, Zhen and Xu, Haiyong and Luo, Ting and Song, Yang and He, Zhouyan, Udaformer: Underwater Image Enhancement Based on Dual Attention Transformer. Available at SSRN: https://ssrn.com/abstract=4162640 Zhen Shen Ningbo University ( email ) China Haiyong Xu (Contact Author) Ningbo University ( email ) China Ting Luo Ningbo University ( email ) China Yang Song Ningbo University of Technology ( email ) China Zhouyan He Ningbo University of Technology ( email ) China 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 0 Abstract Views 0 PlumX Metrics Related eJournals Computation Theory eJournal Follow Computation Theory eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 178 PAPERS 2,816 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-apollo5 in 0.226 seconds