Combining Pre- and Post-Demosaicking Noise Removal for RAW Video
Marco S\'anchez-Beeckman (1), Antoni Buades (1), Nicola Brandonisio, (2), Bilel Kanoun (2) ((1) IAC3 & Departament de Matem\`atiques i, Inform\`atica, Universitat de les Illes Balears, (2) Huawei Technologies, France)

TL;DR
This paper introduces a novel denoising approach for RAW video that combines pre- and post-demosaicking neural networks, leveraging self-similarity and temporal filtering to adapt to various noise levels and improve real-world video quality.
Contribution
It proposes a self-similarity-based scheme that weights pre- and post-demosaicking denoisers, with adaptive influence based on noise levels, and incorporates temporal prefiltering for enhanced texture reconstruction.
Findings
Higher noise levels benefit from increased pre-demosaicking influence.
The method adapts to any noise level using sensor noise estimation.
It achieves competitive results with state-of-the-art denoising techniques.
Abstract
Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video. It is generally performed early in the pipeline, usually before demosaicking, although studies swapping their order or even conducting them jointly have been proposed. With the advent of deep learning, the quality of denoising algorithms has steadily increased. Even so, modern neural networks still have a hard time adapting to new noise levels and scenes, which is indispensable for real-world applications. With those in mind, we propose a self-similarity-based denoising scheme that weights both a pre- and a post-demosaicking denoiser for Bayer-patterned CFA video data. We show that a balance between the two leads to better image quality, and we empirically find that higher noise levels benefit from a higher influence pre-demosaicking.…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · Image Processing and 3D Reconstruction · Digital Media Forensic Detection
