ECAFormer: Low-light Image Enhancement using Cross Attention

• URL: http://arxiv.org/abs/2406.13281v2
• Date: Tue, 20 Aug 2024 07:54:01 GMT
• Title: ECAFormer: Low-light Image Enhancement using Cross Attention
• Authors: Yudi Ruan, Hao Ma, Weikai Li, Xiao Wang,
• Abstract summary: Low-light image enhancement (LLIE) is critical in computer vision. We design a hierarchical mutual Enhancement via a Cross Attention transformer (ECAFormer) We show that ECAFormer reaches competitive performance across multiple benchmarks, yielding nearly a 3% improvement in PSNR over the suboptimal method.
• Score: 11.554554006307836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-light image enhancement (LLIE) is critical in computer vision. Existing LLIE methods often fail to discover the underlying relationships between different sub-components, causing the loss of complementary information between multiple modules and network layers, ultimately resulting in the loss of image details. To beat this shortage, we design a hierarchical mutual Enhancement via a Cross Attention transformer (ECAFormer), which introduces an architecture that enables concurrent propagation and interaction of multiple features. The model preserves detailed information by introducing a Dual Multi-head self-attention (DMSA), which leverages visual and semantic features across different scales, allowing them to guide and complement each other. Besides, a Cross-Scale DMSA block is introduced to capture the residual connection, integrating cross-layer information to further enhance image detail. Experimental results show that ECAFormer reaches competitive performance across multiple benchmarks, yielding nearly a 3% improvement in PSNR over the suboptimal method, demonstrating the effectiveness of information interaction in LLIE.

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