通过分治法修复阴影中的亮度与色彩，提升图像质量。
ShadowHack: Hacking Shadows via Luminance-Color Divide and Conquer
- 将阴影修复分为亮度恢复与色彩还原两步处理
- 在多个数据集上优于现有最佳方法，显著改善视觉效果
- 适合需要高质量图像增强的计算机视觉应用
阴影导致图像亮度降低、纹理退化和色彩失真，难以统一解决。本文提出 ShadowHack，一种分治策略，将任务分解为亮度恢复与色彩修复。为提升阴影区域亮度并修复纹理，设计了带修正注意力模块的 U 形网络 LRNet，增强信息交互并校准受污染的注意力图。亮度恢复后，CRNet 利用交叉注意力机制恢复鲜艳色彩，生成视觉效果优异的结果。在多个数据集上的大量实验表明，ShadowHack 在定量与定性指标上均优于现有最先进方法，验证了其设计的有效性。代码将公开发布。
原文摘要 · Abstract (English)
Shadows introduce challenges such as reduced brightness, texture deterioration, and color distortion in images, complicating a holistic solution. This study presents \textbf{ShadowHack}, a divide-and-conquer strategy that tackles these complexities by decomposing the original task into luminance recovery and color remedy. To brighten shadow regions and repair the corrupted textures in the luminance space, we customize LRNet, a U-shaped network with a rectified attention module, to enhance information interaction and recalibrate contaminated attention maps. With luminance recovered, CRNet then leverages cross-attention mechanisms to revive vibrant colors, producing visually compelling results. Extensive experiments on multiple datasets are conducted to demonstrate the superiority of ShadowHack over existing state-of-the-art solutions both quantitatively and qualitatively, highlighting the effectiveness of our design. Our code will be made publicly available.
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