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arXiv · 1904.10898

ViDeNN: Deep Blind Video Denoising

Michele Claus·Jan van Gemert
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Abstract

We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the noise distribution (blind denoising). The CNN architecture uses a combination of spatial and temporal filtering, learning to spatially denoise the frames first and at the same time how to combine their temporal information, handling objects motion, brightness changes, low-light conditions and temporal inconsistencies. We demonstrate the importance of the data used for CNNs training, creating for this purpose a specific dataset for low-light conditions. We test ViDeNN on common benchmarks and on self-collected data, achieving good results comparable with the state-of-the-art.

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BibTeXRIS

Michele Claus, Jan van Gemert. 2019-04-24. ViDeNN: Deep Blind Video Denoising. https://arxiv.org/abs/1904.10898

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