Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved by fine-tuning a pre-trained AWGN denoising network to the video with a novel frame-to-frame training strategy. Our denoiser can be used without knowledge of the origin of the video or burst and the post processing steps applied from the camera sensor. The on-line process only requires a couple of frames before achieving visually-pleasing results for a wide range of perturbations. It nonetheless reaches state of the art performance for standard Gaussian noise, and can be used off-line with still better performance.
@article{arxiv.1811.12766,
title = {Model-blind Video Denoising Via Frame-to-frame Training},
author = {Thibaud Ehret and Axel Davy and Jean-Michel Morel and Gabriele Facciolo and Pablo Arias},
journal= {arXiv preprint arXiv:1811.12766},
year = {2020}
}