Low-light image enhancement (LLIE) is vital for autonomous driving. Despite
the importance, existing LLIE methods often prioritize robustness in overall
brightness adjustment, which can come at the expense of detail preservation. To
overcome this limitation,we propose the Hierarchical Mutual Enhancement via
Cross-Attention transformer (ECAFormer), a novel network that utilizes Dual
Multi-head Self Attention (DMSA) to enhance both visual and semantic features
across scales, significantly preserving details during the process. The
cross-attention mechanism in ECAFormer not only improves upon traditional
enhancement techniques but also excels in maintaining a balance between global
brightness adjustment and local detail retention. Our extensive experimental
validation on renowned low-illumination datasets, including SID and LOL, and
additional tests on dark road scenarios. or performance over existing methods
in terms of illumination enhancement and noise reduction, while also optimizing
computational complexity and parameter count, further boosting SSIM and PSNR
metrics. Our project is available at this https URL

通过 Hierarchical Mutual Enhancement via Cross-Attention transformer (ECAFormer) 使用 Dual Multi-head Self Attention (DMSA) 提高细节保留，实现低光图像增强，优化计算复杂性和参数数量，提高了照明增强和降噪性能。