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作 者:李梓诺 陈绪行 郭淑娜 王书强 潘治文 Zi-Nuo Li;Xu-Hang Chen;Shu-Na Guo;Shu-Qiang Wang;Chi-Man Pun(Department of Computer and Information Science,University of Macao,Macao 999078,China;Research Center for Biomedical Information Technology,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China)
机构地区:[1]Department of Computer and Information Science,University of Macao,Macao 999078,China [2]Research Center for Biomedical Information Technology,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China
出 处:《Journal of Computer Science & Technology》2024年第2期336-345,共10页计算机科学技术学报(英文版)
基 金:supported in part by the Science and Technology Development Fund of Macao SAR of China under Grant Nos.0087/2020/A2 and 0141/2023/RIA2;the National Natural Science Foundation of China under Grant No.62172403;the Distinguished Young Scholars Fund of Guangdong Province of China under Grant No.2021B1515020019.
摘 要:Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.
关 键 词:TRANSFORMER wavelet transform image enhancement
分 类 号:TP391.41[自动化与计算机技术—计算机应用技术]
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