CLOE: A Confidence-Based Local-To-Global Estimation Framework for Multispectral Illuminant Recovery

This paper introduces CLOE, a confidence-based local-to-global fully convolutional framework for multispectral illuminant estimation, which jointly captures spatial structure and spectral correlations through adaptive confidence-weighted pooling. The architecture integrates a Spatial Feature Extractor and a Spectral Branch through a confidence-based fusion mechanism that produces robust global illuminant predictions. Evaluations on the KAUST and BeyondRGB datasets demonstrate that CLOE consistently outperforms both classical statistical methods and recent learning-based approaches, achieving state-of-the-art performance in terms of angular error between the estimated and ground-truth illuminant spectra (mean-∆AMS and std-∆AMS). Qualitative analyses further show that CLOE provides interpretable intermediate representations, enabling visual inspection of spatial–spectral cues. These results highlight the effectiveness and robustness of the proposed method for real-world multispectral imaging.