NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework

08/06/2023
by   Yuan Tong, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/28/2021

An Efficient Network Design for Face Video Super-resolution

Face video super-resolution algorithm aims to reconstruct realistic face...
research
07/12/2021

Real-Time Super-Resolution System of 4K-Video Based on Deep Learning

Video super-resolution (VSR) technology excels in reconstructing low-qua...
research
05/20/2022

Combining Contrastive and Supervised Learning for Video Super-Resolution Detection

Upscaled video detection is a helpful tool in multimedia forensics, but ...
research
04/16/2021

Multitask Learning for VVC Quality Enhancement and Super-Resolution

The latest video coding standard, called versatile video coding (VVC), i...
research
10/19/2021

ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution

Despite the growing popularity of video super-resolution (VSR), there is...
research
04/29/2023

An Implicit Alignment for Video Super-Resolution

Video super-resolution commonly uses a frame-wise alignment to support t...
research
12/14/2022

Mitigating Artifacts in Real-World Video Super-Resolution Models

The recurrent structure is a prevalent framework for the task of video s...

Please sign up or login with your details

Forgot password? Click here to reset