用自监督特征修复彩色光照下的颜色失真，提升图像还原精度。
CANDLE: Illumination-Invariant Semantic Priors for Color Ambient Lighting Normalization
- 引入多层DINOv3特征作为光照不变语义先验，增强编码器表达。
- 在CL3AN上比最强基线提升1.22 dB PSNR，NTIRE挑战赛获第3名。
- 适合处理复杂光照场景的图像恢复，尤其擅长抑制色彩漂移。
在多色光照下进行颜色环境光照归一化面临严重色偏、高光饱和及材质依赖反射等挑战。现有几何与低级先验难以在光照主导的色偏条件下恢复物体固有颜色。我们观察到DINOv3的自监督特征在有色光照输入与环境光真实图像间保持高度一致性，由此提出CANDLE（Color Ambient Normalization with DINO Layer Enhancement），通过引入DINO全层引导（D.O.G.）自适应将多层DINOv3特征注入后续编码器阶段，并设计颜色频率精修模块（BFACG + SFFB）以抑制解码器端的色度坍缩与细节污染。在CL3AN数据集上，相较最强基线方法实现+1.22 dB PSNR提升。CANDLE在NTIRE 2026 ALN颜色光照挑战赛中位列第3，在白光赛道中以最低FID取得第2名的保真度表现，证实其在色度与亮度主导光照条件下的强泛化能力。代码已开源：https://github.com/ron941/CANDLE。
原文摘要 · Abstract (English)
Color ambient lighting normalization under multi-colored illumination is challenging due to severe chromatic shifts, highlight saturation, and material-dependent reflectance. Existing geometric and low-level priors are insufficient for recovering object-intrinsic color when illumination-induced chromatic bias dominates. We observe that DINOv3's self-supervised features remain highly consistent between colored-light inputs and ambient-lit ground truth, motivating their use as illumination-robust semantic priors. We propose CANDLE (Color Ambient Normalization with DINO Layer Enhancement), which introduces DINO Omni-layer Guidance (D.O.G.) to adaptively inject multi-layer DINOv3 features into successive encoder stages, and a color-frequency refinement design (BFACG + SFFB) to suppress decoder-side chromatic collapse and detail contamination. Experiments on CL3AN show a +1.22 dB PSNR gain over the strongest prior method. CANDLE achieves 3rd place on the NTIRE 2026 ALN Color Lighting Challenge and 2nd place in fidelity on the White Lighting track with the lowest FID, confirming strong generalization across both chromatic and luminance-dominant illumination conditions. Code is available at https://github.com/ron941/CANDLE.
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