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

arXiv 2308.03121 ↗First posted 6 Aug 2023cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 35 references that are in the map. First posted on arXiv 6 Aug 2023.

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32 of 35 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2020End-to-End Object Detection with Transformerscited by 7,414
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  6. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  7. 2017Deformable Convolutional Networkscited by 1,488
  8. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  9. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  10. 2018Deformable ConvNets v2: More Deformable, Better Resultscited by 670
  11. 2017Video Enhancement with Task-Oriented Flowcited by 619
  12. 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 494
20 more
  1. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 342
  2. 2021GMFlow: Learning Optical Flow via Global Matchingcited by 327
  3. 2017Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolationcited by 326
  4. 2020BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyondcited by 304
  5. 2019Depth-Aware Video Frame Interpolationcited by 234
  6. 2018TDAN: Temporally Deformable Alignment Network for Video Super-Resolutioncited by 226
  7. 2017Video Frame Interpolation via Adaptive Separable Convolutioncited by 213
  8. 2020Real-Time Intermediate Flow Estimation for Video Frame Interpolationcited by 213
  9. 2017Video Frame Interpolation via Adaptive Convolutioncited by 158
  10. 2016Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensationcited by 156
  11. 2017Detail-revealing Deep Video Super-resolutioncited by 148
  12. 2019Quadratic video interpolationcited by 145
  13. 2018MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancementcited by 112
  14. 2022VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolutioncited by 101
  15. 2020Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolutioncited by 91
  16. 2021Temporal Modulation Network for Controllable Space-Time Video Super-Resolutioncited by 54
  17. 2020Space-Time-Aware Multi-Resolution Video Enhancementcited by 49
  18. 2022Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learningcited by 12
  19. 2021Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsamplingcited by 9
  20. 2022You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolutioncited by 3

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