In this article we propose a new recursive video denoising method, Non-Local Kalman (NL-Kalman), with high performance. The method is recursive and uses only the current frame and the previous denoised one. It considers the video as a set of overlapping temporal patch trajectories. Following a Bayesian approach each trajectory is modeled as linear dynamic Gaussian model and denoised by a Kalman filter. To estimate its parameters, similar patches are grouped and their trajectories are considered as sharing the same model parameters. The filtering is mainly temporal; non-local spatial similarity is only used to estimate the parameters. This temporally causal method obtains results comparable (in terms of PSNR and SSIM) to state-of-the-art methods using several frames per frame denoised, but with a higher temporal consistency.
Thibaud Ehret, Jean-Michel Morel, Pablo Arias, Non-Local Kalman: a recursive video denoising algorithm ICIP 2018.
Work supported by IDEX Paris-Saclay IDI 2016, ANR-11-IDEX-0003-02, ONR grant N00014-17-1-2552, CNES MISS project, DGA Astrid ANR-17-ASTR-0013-01, DGA ANR-16-DEFA-0004-01, and MENRT.