用自监督视觉先验提升复杂光照下图像恢复效果
DINOLight: Robust Ambient Light Normalization with Self-supervised Visual Prior Integration
- 融合DINOv2多层特征，通过自适应加权实现光照信息提取
- 在Ambient6K数据集上优于现有方法，显著改善非均匀光照还原
- 无需额外标注，适合低光照、多光源场景的图像修复任务
本文提出一种新的环境光照归一化框架DINOLight，将自监督模型DINOv2的图像理解能力作为视觉先验引入恢复过程。该任务旨在修复由多重光源和复杂场景结构导致的非均匀阴影与光照失真。实验发现，DINOv2能可靠提取退化图像中的语义与几何信息。基于此，我们设计了一种新框架，利用DINOv2特征进行光照归一化。首先，提出一种自适应特征融合模块，通过逐点softmax掩码融合不同层DINOv2特征；其次，通过辅助交叉注意力机制，将融合特征在空间与频率域同时嵌入恢复网络。实验表明，DINOLight在Ambient6K数据集上表现优异，且DINOv2特征有效提升了光照归一化性能。此外，在阴影去除基准数据集上也取得与使用掩码先验方法相当的结果。代码将在录用后公开。
原文摘要 · Abstract (English)
This paper presents a new ambient light normalization framework, DINOLight, that integrates the self-supervised model DINOv2's image understanding capability into the restoration process as a visual prior. Ambient light normalization aims to restore images degraded by non-uniform shadows and lighting caused by multiple light sources and complex scene geometries. We observe that DINOv2 can reliably extract both semantic and geometric information from a degraded image. Based on this observation, we develop a novel framework to utilize DINOv2 features for lighting normalization. First, we propose an adaptive feature fusion module that combines features from different DINOv2 layers using a point-wise softmax mask. Next, the fused features are integrated into our proposed restoration network in both spatial and frequency domains through an auxiliary cross-attention mechanism. Experiments show that DINOLight achieves superior performance on the Ambient6K dataset, and that DINOv2 features are effective for enhancing ambient light normalization. We also apply our method to shadow-removal benchmark datasets, achieving competitive results compared to methods that use mask priors. Codes will be released upon acceptance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。