Graph-Based Spectral Attention with Multi-Spectral Images for Illuminant Estimation

Existing color constancy methods based on deep learning primarily rely on the RGB domain and often struggle with accurate illuminant estimation in scenes with minimal spatial information, such as monochromatic environments, leading to suboptimal performance. To address this issue, this paper introduces an approach that utilizes MS (multi-spectral) images estimated by a pretrained RGB-to-MS model, enabling more accurate illuminant estimation. Additionally, we propose a graph-based spectral attention mechanism designed to effectively extract spectral features within the multi-spectral domain, further enhancing the robustness and accuracy of color constancy. This approach demonstrates outstanding effectiveness on our custom dataset, significantly outperforming existing methods. Additionally, when evaluated in the widely recognized NUS-8 and Cube+ datasets, the proposed method shows a substantial relative improvement of 21.5% in NUS-8 and 9.9% in Cube+ compared to previous state-of-the-art methods.