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 outreach 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 at https://github.com/lime-j/ShadowHack

翻译：阴影在图像中会引发亮度降低、纹理退化及色彩失真等挑战，使得整体解决方案的设计变得复杂。本研究提出\textbf{ShadowHack}，一种分治策略，通过将原始任务分解为亮度恢复与色彩校正来处理这些复杂问题。为提升阴影区域亮度并修复亮度空间中受损的纹理，我们定制了LRNet——一种包含修正外展注意力模块的U形网络，以增强信息交互并重新校准受污染的注意力图。在亮度恢复后，CRNet进一步利用交叉注意力机制还原鲜艳色彩，生成视觉上引人注目的结果。我们在多个数据集上进行了广泛实验，定量与定性结果均表明ShadowHack优于现有先进方法，凸显了本设计的有效性。代码将在https://github.com/lime-j/ShadowHack 公开。