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<div align="center"> Chasing Faster ConvNet for Efficient Super-Resolution </div>
Overview: The repository records a path of chasing faster ConvNet.
The repo is still under construction!
☁️ EFDN for NTIRE 2022 ESR
<a href="https://arxiv.org/abs/2204.08759" alt="arXiv"> <img src="https://img.shields.io/badge/arXiv-2204.08759-b31b1b.svg?style=flat" /></a> <a href="https://data.vision.ee.ethz.ch/cvl/ntire22/posters/Wang_Edge_074-poster-Edge-enhanced%20Feature%20Distillation%20Network%20for%20Efficient%20Super-Resolution.pdf" alt="Poster"> <img src="https://img.shields.io/badge/poster-NTIRE 2022-brightgreen" /></a> <a href="https://github.com/icandle/EFDN" alt="Poster"> <img src="https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fatrox%2Fsync-dotenv%2Fbadge%3Fref%3Dmaster&style=flat" /></a> </p>Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
Yan Wang
Nankai University
Summary: 5th solution of Model Complexity in the NTIRE 2022 Challenge on Efficient Super-Resolution. Involoving the modification of convolution and network architecture.
- 🌟 Convolution: edge-ehanced reparameter block (EDBB) with a corresponding edge loss .
- 📦 Attention: original ESA.
- 📦 Backbone: backbone searched by network-level NAS.
🌥️ PFDN for NTIRE 2023 ESR
<a href="https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/papers/Li_NTIRE_2023_Challenge_on_Efficient_Super-Resolution_Methods_and_Results_CVPRW_2023_paper" alt="Report"> <img src="https://img.shields.io/badge/report-NTIRE 2023-367DBD" /></a> <a href="https://github.com/icandle/PlainUSR/blob/main/2023_PFDN_NTIRE/factsheet/08-PFDN-Factsheet.pdf"> <img src="https://img.shields.io/badge/docs-factsheet-8A2BE2" /></a> <a href="https://github.com/icandle/PlainUSR/blob/main/models/team08_PFDN.py" alt="Report"> <img src="https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fatrox%2Fsync-dotenv%2Fbadge%3Fref%3Dmaster&style=flat" /></a> </p>Partial Feature Distillation Network for Efficient Super-Resolution
Yan Wang, Erlin Pan, Qixuan Cai, Xinan Dai
Nankai University, University of Electronic Science and Technology of China, Tianjin University
Summary: Winner of Overall Evaluation and 4th of Runtime in the NTIRE 2023 Challenge on Efficient Super-Resolution. Involoving the modification of convolution and network architecture.
- ⭐️ Convolution: integrating partial convolution and RRRB.
- 📦 Attention: efficient ESA.
- 📦 Backbone: ResNet-style backbone.
| <sub> Model </sub> | <sub> Runtime[ms] </sub> | <sub> Params[M] </sub> | <sub> Flops[G] </sub> | <sub> Acts[M] </sub> | <sub> GPU Mem[M] </sub> |
|---|---|---|---|---|---|
| RFDN | 35.54 | 0.433 | 27.10 | 112.03 | 788.13 |
| PFDN | 20.49 | 0.272 | 16.76 | 65.10 | 296.45 |
⛅️ PFDNLite for NTIRE 2024 ESR
<a href="https://openaccess.thecvf.com/content/CVPR2024W/NTIRE/papers/Ren_The_Ninth_NTIRE_2024_Efficient_Super-Resolution_Challenge_Report_CVPRW_2024_paper.pdf" alt="Report"> <img src="https://img.shields.io/badge/report-NTIRE 2024-367DBD" /></a> <a href="https://github.com/icandle/BSR/blob/main/factsheet/NTIRE_2024_ESR.pdf"> <img src="https://img.shields.io/badge/docs-factsheet-8A2BE2" /></a> <a href="https://github.com/icandle/BSR" alt="Report"> <img src="https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fatrox%2Fsync-dotenv%2Fbadge%3Fref%3Dmaster&style=flat" /></a> </p>Lightening Partial Feature Distillation Network for Efficient Super-Resolution
Yan Wang, Yi Liu, Qing Wang, Gang Zhang, Liou Zhang, Shijie Zhao
Nankai University, ByteDance
Summary: 3rd of Overall Evaluation and 3rd of Runtime in the NTIRE 2024 Challenge on Efficient Super-Resolution. Involoving the modification of convolution, attention and network pruning.
- 📦 Convolution: RepMBConv in PlainUSR.
- 📦 Attention: LIA in PlainUSR.
- ⭐️ Backbone: ABPN-style backbone and block pruning.
To be updated.
🌤️ PlainUSR for ACCV 2024
<a href="https://arxiv.org/abs/2409.13435" alt="arXiv"> <img src="https://img.shields.io/badge/arXiv-2409.13435-b31b1b.svg?style=flat" /></a> <a href="https://github.com/icandle/PlainUSR/blob/main/LICENSE" alt="license"> <img src="https://img.shields.io/badge/license-MIT--License-%23B7A800" /></a> </p>PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution
Yan Wang, Yusen Li<sup>†</sup>, Gang Wang, Xiaoguang Liu
Nankai University
Summary: we present PlainUSR incorporating three pertinent modifications (convolution, attention, and backbone) to expedite ConvNet for efficient SR.
- 🌟 Convolution: Reparameterized MobileNetV3 Convolution (RepMBConv).
- ⭐️ Attention: Local Importance-based Attention (LIA).
- 🌟 Backbone: Plain U-Net.
To be updated.
☀️ PlainUSRv2
To be updated.
💖 Acknowledgments
We would thank BasicSR, ECBSR, DBB, ETDS, FasterNet, etc, for their enlightening work!
🎓 Citation
@inproceedings{wang2022edge,
title={Edge-enhanced Feature Distillation Network for Efficient Super-Resolution},
author={Wang, Yan},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
pages={777--785},
year={2022}
}
@article{wang2024plainusr,
title={PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution},
author={Wang, Yan and Li, Yusen and Wang, Gang and Liu, Xiaoguang},
journal={arXiv preprint arXiv:2409.13435},
year={2024}
}