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vsr

A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.

https://github.com/loseall/videosuperresolution

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    Links to: arxiv.org, ieee.org
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    Scientific vocabulary similarity
    Low similarity (7.8%) to scientific vocabulary

Keywords

carn dbpn dncnn edsr frvsr ntire2019 pytorch rcan rdn srcnn srgan srmd super-resolution tensorflow vdsr vespcn vsr
Last synced: 6 months ago · JSON representation

Repository

A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.

Basic Info
  • Host: GitHub
  • Owner: LoSealL
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 9.04 MB
Statistics
  • Stars: 1,658
  • Watchers: 53
  • Forks: 299
  • Open Issues: 8
  • Releases: 0
Topics
carn dbpn dncnn edsr frvsr ntire2019 pytorch rcan rdn srcnn srgan srmd super-resolution tensorflow vdsr vespcn vsr
Created over 7 years ago · Last pushed over 5 years ago
Metadata Files
Readme Changelog License

README.md

Video Super Resolution

A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.

Project uploaded to PyPI now. Try install from PyPI: shell script pip install VSR

Pretrained weights is uploading now.

Several referenced PyTorch implementations are also included now.

Quick Link: - Installation - Getting Started - Benchmark

Network list and reference (Updating)

The hyperlink directs to paper site, follows the official codes if the authors open sources.

All these models are implemented in ONE framework.

|Model |Published |Code* |VSR (TF)**|VSR (Torch)|Keywords|Pretrained| |:-----|:---------|:-----|:---------|:----------|:-------|:---------| |SRCNN|ECCV14|-, Keras|Y|Y| Kaiming |√| |RAISR|arXiv|-|-|-| Google, Pixel 3 || |ESPCN|CVPR16|-, Keras|Y|Y| Real time |√| |VDSR|CVPR16|-|Y|Y| Deep, Residual |√| |DRCN|CVPR16|-|Y|Y| Recurrent || |DRRN|CVPR17|Caffe, PyTorch|Y|Y| Recurrent || |LapSRN|CVPR17|Matlab|Y|-| Huber loss || |EDSR|CVPR17|-|Y|Y| NTIRE17 Champion |√| |SRGAN|CVPR17|-|Y|-| 1st proposed GAN || |VESPCN|CVPR17|-|Y|Y| VideoSR |√| |MemNet|ICCV17|Caffe|Y|-||| |SRDenseNet|ICCV17|-, PyTorch|Y|-| Dense |√| |SPMC|ICCV17|Tensorflow|T|Y| VideoSR || |DnCNN|TIP17|Matlab|Y|Y| Denoise |√| |DCSCN|arXiv|Tensorflow|Y|-||| |IDN|CVPR18|Caffe|Y|-| Fast |√| |RDN|CVPR18|Torch|Y|-| Deep, BI-BD-DN || |SRMD|CVPR18|Matlab|-|Y| Denoise/Deblur/SR |√| |DBPN|CVPR18|PyTorch|Y|Y| NTIRE18 Champion |√| |ZSSR|CVPR18|Tensorflow|-|-| Zero-shot || |FRVSR|CVPR18|PDF|T|Y| VideoSR |√| |DUF|CVPR18|Tensorflow|T|-| VideoSR || |CARN|ECCV18|PyTorch|Y|Y| Fast |√| |RCAN|ECCV18|PyTorch|Y|Y| Deep, BI-BD-DN || |MSRN|ECCV18|PyTorch|Y|Y| |√| |SRFeat|ECCV18|Tensorflow|Y|Y| GAN || |NLRN|NIPS18|Tensorflow|T|-| Non-local, Recurrent || |SRCliqueNet|NIPS18|-|-|-| Wavelet || |FFDNet|TIP18|Matlab|Y|Y| Conditional denoise|| |CBDNet|CVPR19|Matlab|T|-| Blind-denoise || |SOFVSR|ACCV18|PyTorch|-|Y| VideoSR |√| |ESRGAN|ECCVW18|PyTorch|-|Y|1st place PIRM 2018|√| |TecoGAN|arXiv|Tensorflow|-|T| VideoSR GAN|√| |RBPN|CVPR19|PyTorch|-|Y| VideoSR |√| |DPSR|CVPR19|Pytorch|-|-||| |SRFBN|CVPR19|Pytorch|-|-|||| |SRNTT|CVPR19|Tensorflow|-|-|Adobe|| |SAN|CVPR19|empty|-|-| AliDAMO SOTA || |AdaFM|CVPR19|Pytorch|-|-| SenseTime Oral ||

*The 1st repo is by paper author.

**Y: included; -: not included; T: under-testing.

You can download pre-trained weights through prepare_data, or visit the hyperlink at √.

Link of datasets

(please contact me if any of links offend you or any one disabled)

|Name|Usage|#|Site|Comments| |:---|:----|:----|:---|:-----| |SET5|Test|5|download|jbhuang0604| |SET14|Test|14|download|jbhuang0604| |SunHay80|Test|80|download|jbhuang0604| |Urban100|Test|100|download|jbhuang0604| |VID4|Test|4|download|4 videos| |BSD100|Train|300|download|jbhuang0604| |BSD300|Train/Val|300|download|-| |BSD500|Train/Val|500|download|-| |91-Image|Train|91|download|Yang| |DIV2K|Train/Val|900|website|NTIRE17| |Waterloo|Train|4741|website|-| |MCL-V|Train|12|website|12 videos| |GOPRO|Train/Val|33|website|33 videos, deblur| |CelebA|Train|202599|website|Human faces| |Sintel|Train/Val|35|website|Optical flow| |FlyingChairs|Train|22872|website|Optical flow| |DND|Test|50|website|Real noisy photos| |RENOIR|Train|120|website|Real noisy photos| |NC|Test|60|website|Noisy photos| |SIDD(M)|Train/Val|200|website|NTIRE 2019 Real Denoise| |RSR|Train/Val|80|download|NTIRE 2019 Real SR| |Vimeo-90k|Train/Test|89800|website|90k HQ videos|

Other open datasets: Kaggle ImageNet COCO

VSR package

This package offers a training and data processing framework based on TF. What I made is a simple, easy-to-use framework without lots of encapulations and abstractions. Moreover, VSR can handle raw NV12/YUV as well as a sequence of images as inputs.

Install

  1. Prepare proper tensorflow and pytorch(optional). For example, GPU and CUDA10.0 (recommend to use conda):

shell conda install tensorflow-gpu==1.15.0 # optional # conda install pytorch

  1. Install VSR package

bash # For someone see this doc online # git clone https://github.com/loseall/VideoSuperResolution && cd VideoSuperResolution pip install -e .

Getting Started

  1. Download pre-trained weights and (optinal) training datasets. For instance, let\'s begin with VESPCN and vid4 test data: shell python prepare_data.py --filter vespcn vid4

  2. Customize backend cd ~/.vsr/ touch config.yml yaml backend: tensorflow # (tensorflow, pytorch) verbose: info # (debug, info, warning, error)

  3. Evaluate shell cd Train python eval.py srcnn -t vid4 --pretrain=/path/srcnn.pth

  4. Train shell python prepare_data.py --filter mcl-v cd Train python train.py vespcn --dataset mcl-v --memory_limit 1GB --epochs 100

OK, that's all you need. For more details, use --help to get more information.

More documents can be found at Docs.

Owner

  • Name: Tang, Wenyi
  • Login: LoSealL
  • Kind: user
  • Location: Chengdu, PRC
  • Company: Intel

DL researcher; Low-level CV; Video enhancement; ML kernel coder

GitHub Events

Total
  • Watch event: 50
  • Fork event: 7
Last Year
  • Watch event: 50
  • Fork event: 7

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 414
  • Total Committers: 2
  • Avg Commits per committer: 207.0
  • Development Distribution Score (DDS): 0.005
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
tangwy's nuc t****5@l****m 412
Raphaël Zumer r****r@t****t 2
Committer Domains (Top 20 + Academic)
tebako.net: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 79
  • Total pull requests: 22
  • Average time to close issues: 3 months
  • Average time to close pull requests: about 2 hours
  • Total issue authors: 49
  • Total pull request authors: 2
  • Average comments per issue: 5.38
  • Average comments per pull request: 0.05
  • Merged pull requests: 22
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
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  • SandLeg (5)
  • Undercut (3)
  • anqin5211314 (3)
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Pull Request Authors
  • LoSealL (21)
  • rzumer (1)
Top Labels
Issue Labels
help wanted (5) bug (3) enhancement (2)
Pull Request Labels
enhancement (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 96 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 1
pypi.org: vsr

Video Super-Resolution Framework

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 96 Last month
Rankings
Stargazers count: 1.7%
Forks count: 3.0%
Dependent packages count: 9.8%
Average: 11.9%
Dependent repos count: 21.8%
Downloads: 23.2%
Maintainers (1)
LoSealL
Last synced: 7 months ago