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ViDeNN: Deep Blind Video Denoising

24 Apr 2019arXiv:1904.10898archive 2025-07-28

Michele Claus, Jan van Gemert

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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DenoisingVideo Denoising

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Color Image Denoising CBSD68 sigma10 Spatial-CNN PSNR 35.92 #2 of 2 Archive leaderboard report
Color Image Denoising CBSD68 sigma15 Spatial-CNN PSNR 33.66 #10 of 10 Archive leaderboard report
Color Image Denoising CBSD68 sigma25 Spatial-CNN PSNR 30.99 #8 of 9 Archive leaderboard report
Color Image Denoising CBSD68 sigma35 Spatial-CNN PSNR 29.34 #4 of 6 Archive leaderboard report
Color Image Denoising CBSD68 sigma5 Spatial-CNN PSNR 39.73 #3 of 3 Archive leaderboard report
Color Image Denoising CBSD68 sigma50 Spatial-CNN PSNR 27.63 #15 of 18 Archive leaderboard report

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