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Papers/ViDeNN: Deep Blind Video Denoising

ViDeNN: Deep Blind Video Denoising

Michele Claus, Jan van Gemert

2019-04-24DenoisingVideo Denoising
PaperPDFCode

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.

Results

TaskDatasetMetricValueModel
DenoisingCBSD68 sigma5PSNR39.73Spatial-CNN
DenoisingCBSD68 sigma15PSNR33.66Spatial-CNN
DenoisingCBSD68 sigma25PSNR30.99Spatial-CNN
DenoisingCBSD68 sigma35PSNR29.34Spatial-CNN
DenoisingCBSD68 sigma10PSNR35.92Spatial-CNN
DenoisingCBSD68 sigma50PSNR27.63Spatial-CNN
3D ArchitectureCBSD68 sigma5PSNR39.73Spatial-CNN
3D ArchitectureCBSD68 sigma15PSNR33.66Spatial-CNN
3D ArchitectureCBSD68 sigma25PSNR30.99Spatial-CNN
3D ArchitectureCBSD68 sigma35PSNR29.34Spatial-CNN
3D ArchitectureCBSD68 sigma10PSNR35.92Spatial-CNN
3D ArchitectureCBSD68 sigma50PSNR27.63Spatial-CNN

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