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Empowering Image Recovery_ A Multi-Attention Approach
April 9, 2024, 4:46 a.m. | Juan Wen (Zhengzhou University, Computer Vision Lab, ETH Zurich), Yawei Li (Computer Vision Lab, ETH Zurich), Chao Zhang (LAN-XEN, Technology, INC), W
cs.CV updates on arXiv.org arxiv.org
Abstract: We propose Diverse Restormer (DART), a novel image restoration method that effectively integrates information from various sources (long sequences, local and global regions, feature dimensions, and positional dimensions) to address restoration challenges. While Transformer models have demonstrated excellent performance in image restoration due to their self-attention mechanism, they face limitations in complex scenarios. Leveraging recent advancements in Transformers and various attention mechanisms, our method utilizes customized attention mechanisms to enhance overall performance. DART, our novel …
abstract arxiv attention challenges cs.cv dart dimensions diverse face feature global image image restoration information novel performance self-attention transformer transformer models type
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