Fast Fourier Color Constancy

We present Fast Fourier Color Constancy (FFCC), a novel color constancy algorithm which works by reformulating the problem of illuminant estimation into a spatial localization task on a torus. On standard benchmarks, our model produces lower error rates than the previous state-of-the-art by 13 - 20%, while also being 250 - 3000 times faster. This speed and accuracy is primarily due to how FFCC primarily operates in the frequency domain, though this approach also introduces a set of new difficulties regarding aliasing, directional statistics and preconditioning, which we address. Unlike past work, our model produces a complete posterior distribution over illuminants instead of a single illuminant estimate, which allows for a richer analysis and enables a novel temporal smoothing technique. FFCC is capable of running at ~ 700 frames per second on a mobile phone, making it a viable solution to the problem of constructing an effective, real-time, temporally-coherent automatic white balance algorithm.

• Machine Intelligence

• Machine Perception