Computational color constancy (CC) has been specially designed to recover the true color of scene by first inferring the light source color and then discounting it from the captured color biased image. However, the image colors are determined not only by the illuminant but also by the camera spectral sensitivity (CSS). This paper studies the CSS effects on CC performance by showing the insufficiency of existing learning-based CC algorithms when dealing with the situation of so called inter-dataset-based CC, i.e., training a CC model on one dataset and then testing it on another dataset that was captured by a distinct CSS. This is a key drawback that limits the wide application of many existing CC models. To overcome this problem, we propose a simple way to solve this problem by firstly learning a transform matrix that converts the responses to the illuminant and images under one CSS into those under another CSS. Then, the learned matrix is used to transform the data rendered under one CSS into another specific CSS before applying the CC model on another dataset. Theoretical analysis and experimental validation on synthetic, hyperspectral, and real camera captured images show that the proposed method can significantly improve the inter-dataset-based performance for traditional CC algorithms. Thus, we suggest that for better generalization of the state-of-the-art CC algorithms, the effect of CSS should be reasonably considered.
['Shaobing Gao', 'Ming Zhang', 'Chao-Yi Li', 'Yongjie Li']
Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity