Signal-dependent noise removal for color videos using temporal and cross-channel priors

• We report a novel color video denoising method that outperforms state-of-the-arts.
• We propose a new cross-channel prior to suppress color fringing artifacts.
• We use the temporal prior to separate thin structures from large noise.
• We model video noise as signal dependent with a Poisson–Gaussian noise model.
• We incorporate the two priors and noise model into a joint optimization framework.

Noise widely exists in video acquisition, and is especially large under low illumination conditions. Existing video denoising methods are usually at the risk of losing perceptually crucial scene details and introducing unpleasant artifacts. Inspired by high sensitivity of human vision system to thin structures and color aberration in natural images, we incorporate two video priors into a joint optimization framework besides the constraint from the adopted Poisson–Gaussian noise model: (i) we force the motion compensated frames to be a low rank matrix to separate thin structures from large noise. (ii) we utilize the consistency of image pixel gradients in different color channels as a cross channel prior to eliminate color fringing artifacts. To solve this non-convex optimization model, we derive a numerical algorithm via the augmented Lagrangian multiplier method. The effectiveness of our approach is validated by a series of experiments, with both objective and subjective evaluations.