ECAFormer: Low-light Image Enhancement using Cross Attention
<|reference_start|>ECAFormer: Low-light Image Enhancement using Cross Attention: 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.<|reference_end|>
@article{ruan2024ecaformer:,
    title={ECAFormer: Low-light Image Enhancement using Cross Attention},
    author={Yudi Ruan, Hao Ma, Weikai Li and Xiao Wang},
    journal={arXiv preprint arXiv:2406.13281},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13281},
    primaryClass={cs.CV}
}