DOI

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 lowlight conditions. We test ViDeNN on common benchmarks and on self-collected data, achieving good results comparable with the state-of-the-art.
Original languageEnglish
Title of host publicationCVPR workshop on NTIRE: New Trends in Image Restoration and Enhancement
Pages1843-1852
Number of pages10
ISBN (Electronic)978-1-7281-2506-0
DOIs
Publication statusPublished - 2019
EventCVPR workshop on NTIRE: New Trends in Image Restoration and Enhancement: NTIRE 2019 - Long Beach, United States
Duration: 17 Jun 201917 Jun 2019

Workshop

WorkshopCVPR workshop on NTIRE: New Trends in Image Restoration and Enhancement
CountryUnited States
CityLong Beach
Period17/06/1917/06/19

ID: 68855760