A Convolutional Framework for Color Constancy.

We introduce a convolutional framework (CF) for computational color constancy, building upon the established low-level image feature-based framework, which utilized simple image statistics for illuminant estimation. Our framework expands upon this through an end-to-end learnable neural architecture. This adaptation enables the learning and usage of advanced filters that are not restricted to Gaussian kernels operating on individual color channels, thus generalizing the capabilities of the original framework. Additionally, our general framework supports deeper convolutional architectures, thus increasing its computational power. It can also be efficiently applied to estimate multiple spatially varying illuminants within a single scene. Our experimental results on standard datasets demonstrate that the CF outperforms the best methods in the low-level framework, improving the illuminant estimation accuracy by up to 34% for single illuminant estimation and 30% for multiple illuminants estimation. Additionally, our framework exhibits superior performance even when the number of training images is reduced. Finally, we document the inference speedup of our implementation reaching up to 30 × , making the CF especially suitable for applications where efficiency is critical. Source code and trained models available at: https://github.com/MarcoBauzz/convolutional-color-constancy.