ViDeNN: Deep Blind Video Denoising
This addresses video denoising in challenging conditions like low-light and motion for applications in video processing, though it appears incremental as it builds on existing CNN methods.
The authors tackled the problem of blind video denoising without prior noise knowledge by proposing ViDeNN, a CNN that combines spatial and temporal filtering, achieving results comparable to state-of-the-art on benchmarks and self-collected data.
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.