NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework
This work addresses a gap for video processing practitioners by enabling easier application of neural networks in workflows, though it is incremental as it builds on existing frameworks without introducing new algorithms.
The authors tackled the integration of neural network-based video enhancement tasks, such as denoising and super resolution, into video processing pipelines by developing NNVISR, an open-source plugin for VapourSynth that handles network-agnostic details and supports various enhancement types.
We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://github.com/tongyuantongyu/vs-NNVISR.