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We have merged EDVR into MMSR 😄

MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.

EDVR [BasicSR][DNI]

Paper | Project Page | Open VideoRestoration Doc (under construction)

Video Restoration with Enhanced Deformable Convolutional Networks

By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy

EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).

Highlights

  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training

Updates

[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here.
[2019-06-28] Support TOFlow testing (SR) (converted from officially released models).
[2019-06-12] Add training codes.
[2019-06-11] Add data preparation in wiki.
[2019-06-07] Support DUF testing (converted from officially released models).
[2019-05-28] Release testing codes.

Dependencies and Installation

  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it. 
    cd ./codes/models/archs/dcn
python setup.py develop
  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:
    • PyTorch >= 1.1: pip install tb-nightly future

Dataset Preparation

We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.

Get Started

Please see wiki for the basic usage, i.e., training and testing.

Model Zoo and Baselines

Results and pre-trained models are available in the wiki-Model zoo.

Contributing

We appreciate all contributions. Please refer to mmdetection for contributing guideline.

Python code style
We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.

Before you create a PR, make sure that your code lints and is formatted by yapf.

Citation

@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

About

No description, website, or topics provided.

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Readme
Apache-2.0 license
Activity

Stars

0 stars

Watchers

1 watching

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0 forks
Report repository

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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
We have merged EDVR into MMSR 😄
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.
EDVR [BasicSR][DNI]
Paper | Project Page | Open VideoRestoration Doc (under construction)
Video Restoration with Enhanced Deformable Convolutional Networks
By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
Highlights
  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training
Updates
[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here. 

[2019-06-28] Support TOFlow testing (SR) (converted from officially released models). 

[2019-06-12] Add training codes.

[2019-06-11] Add data preparation in wiki.

[2019-06-07] Support DUF testing (converted from officially released models). 

[2019-05-28] Release testing codes.
Dependencies and Installation
  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it.

    cd ./codes/models/archs/dcn
python setup.py develop

  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:

    • PyTorch >= 1.1: pip install tb-nightly future
Dataset Preparation
We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.
Get Started
Please see wiki for the basic usage, i.e., training and testing.
Model Zoo and Baselines
Results and pre-trained models are available in the wiki-Model zoo.
Contributing
We appreciate all contributions. Please refer to mmdetection for contributing guideline.
Python code style

We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.
Before you create a PR, make sure that your code lints and is formatted by yapf.
Citation
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

About
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Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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Repository files navigation
We have merged EDVR into MMSR 😄
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.
EDVR [BasicSR][DNI]
Paper | Project Page | Open VideoRestoration Doc (under construction)
Video Restoration with Enhanced Deformable Convolutional Networks
By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
Highlights
  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training
Updates
[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here. 

[2019-06-28] Support TOFlow testing (SR) (converted from officially released models). 

[2019-06-12] Add training codes.

[2019-06-11] Add data preparation in wiki.

[2019-06-07] Support DUF testing (converted from officially released models). 

[2019-05-28] Release testing codes.
Dependencies and Installation
  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it.

    cd ./codes/models/archs/dcn
python setup.py develop

  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:

    • PyTorch >= 1.1: pip install tb-nightly future
Dataset Preparation
We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.
Get Started
Please see wiki for the basic usage, i.e., training and testing.
Model Zoo and Baselines
Results and pre-trained models are available in the wiki-Model zoo.
Contributing
We appreciate all contributions. Please refer to mmdetection for contributing guideline.
Python code style

We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.
Before you create a PR, make sure that your code lints and is formatted by yapf.
Citation
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
0 stars
Watchers
1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
We have merged EDVR into MMSR 😄
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.
EDVR [BasicSR][DNI]
Paper | Project Page | Open VideoRestoration Doc (under construction)
Video Restoration with Enhanced Deformable Convolutional Networks
By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
Highlights
  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training
Updates
[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here. 

[2019-06-28] Support TOFlow testing (SR) (converted from officially released models). 

[2019-06-12] Add training codes.

[2019-06-11] Add data preparation in wiki.

[2019-06-07] Support DUF testing (converted from officially released models). 

[2019-05-28] Release testing codes.
Dependencies and Installation
  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it.

    cd ./codes/models/archs/dcn
python setup.py develop

  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:

    • PyTorch >= 1.1: pip install tb-nightly future
Dataset Preparation
We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.
Get Started
Please see wiki for the basic usage, i.e., training and testing.
Model Zoo and Baselines
Results and pre-trained models are available in the wiki-Model zoo.
Contributing
We appreciate all contributions. Please refer to mmdetection for contributing guideline.
Python code style

We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.
Before you create a PR, make sure that your code lints and is formatted by yapf.
Citation
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

About
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Readme
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1 watching
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Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
We have merged EDVR into MMSR 😄
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.
EDVR [BasicSR][DNI]
Paper | Project Page | Open VideoRestoration Doc (under construction)
Video Restoration with Enhanced Deformable Convolutional Networks
By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
Highlights
  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training
Updates
[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here. 

[2019-06-28] Support TOFlow testing (SR) (converted from officially released models). 

[2019-06-12] Add training codes.

[2019-06-11] Add data preparation in wiki.

[2019-06-07] Support DUF testing (converted from officially released models). 

[2019-05-28] Release testing codes.
Dependencies and Installation
  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it.

    cd ./codes/models/archs/dcn
python setup.py develop

  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:

    • PyTorch >= 1.1: pip install tb-nightly future
Dataset Preparation
We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.
Get Started
Please see wiki for the basic usage, i.e., training and testing.
Model Zoo and Baselines
Results and pre-trained models are available in the wiki-Model zoo.
Contributing
We appreciate all contributions. Please refer to mmdetection for contributing guideline.
Python code style

We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.
Before you create a PR, make sure that your code lints and is formatted by yapf.
Citation
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
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try {
var __m = "*";
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Repository files navigation
We have merged EDVR into MMSR 😄
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.
EDVR [BasicSR][DNI]
Paper | Project Page | Open VideoRestoration Doc (under construction)
Video Restoration with Enhanced Deformable Convolutional Networks
By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
Highlights
  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training
Updates
[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here. 

[2019-06-28] Support TOFlow testing (SR) (converted from officially released models). 

[2019-06-12] Add training codes.

[2019-06-11] Add data preparation in wiki.

[2019-06-07] Support DUF testing (converted from officially released models). 

[2019-05-28] Release testing codes.
Dependencies and Installation
  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it.

    cd ./codes/models/archs/dcn
python setup.py develop

  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:

    • PyTorch >= 1.1: pip install tb-nightly future
Dataset Preparation
We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.
Get Started
Please see wiki for the basic usage, i.e., training and testing.
Model Zoo and Baselines
Results and pre-trained models are available in the wiki-Model zoo.
Contributing
We appreciate all contributions. Please refer to mmdetection for contributing guideline.
Python code style

We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.
Before you create a PR, make sure that your code lints and is formatted by yapf.
Citation
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
We have merged EDVR into MMSR 😄
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.
EDVR [BasicSR][DNI]
Paper | Project Page | Open VideoRestoration Doc (under construction)
Video Restoration with Enhanced Deformable Convolutional Networks
By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
Highlights
  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training
Updates
[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here. 

[2019-06-28] Support TOFlow testing (SR) (converted from officially released models). 

[2019-06-12] Add training codes.

[2019-06-11] Add data preparation in wiki.

[2019-06-07] Support DUF testing (converted from officially released models). 

[2019-05-28] Release testing codes.
Dependencies and Installation
  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it.

    cd ./codes/models/archs/dcn
python setup.py develop

  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:

    • PyTorch >= 1.1: pip install tb-nightly future
Dataset Preparation
We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.
Get Started
Please see wiki for the basic usage, i.e., training and testing.
Model Zoo and Baselines
Results and pre-trained models are available in the wiki-Model zoo.
Contributing
We appreciate all contributions. Please refer to mmdetection for contributing guideline.
Python code style

We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.
Before you create a PR, make sure that your code lints and is formatted by yapf.
Citation
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

About
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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3 Commits
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README.md
codes
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Repository files navigation
We have merged EDVR into MMSR 😄
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. MMSR is based on our previous projects: BasicSR, ESRGAN, and EDVR.
EDVR [BasicSR][DNI]
Paper | Project Page | Open VideoRestoration Doc (under construction)
Video Restoration with Enhanced Deformable Convolutional Networks
By Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
EDVR won all four tracks in NTIRE 2019 Challenges on Video Restoration and Enhancement (CVPR19 Workshops).
Highlights
  • A unified framework suitable for various video restoration tasks, e.g., super-resolution, deblurring, denoising, etc
  • State of the art: Winners in NTIRE 2019 Challenges on Video Restoration and Enhancement
  • Multi-GPU (distributed) training
Updates
[2019-06-28] Provide training logs and pretrained model for EDVR-M. Check here. 

[2019-06-28] Support TOFlow testing (SR) (converted from officially released models). 

[2019-06-12] Add training codes.

[2019-06-11] Add data preparation in wiki.

[2019-06-07] Support DUF testing (converted from officially released models). 

[2019-05-28] Release testing codes.
Dependencies and Installation
  • Python 3 (Recommend to use Anaconda)
  • PyTorch >= 1.1
  • NVIDIA GPU + CUDA
  • Deformable Convolution. We use mmdetection's dcn implementation. Please first compile it.

    cd ./codes/models/archs/dcn
python setup.py develop

  • Python packages: pip install numpy opencv-python lmdb pyyaml
  • TensorBoard:

    • PyTorch >= 1.1: pip install tb-nightly future
Dataset Preparation
We use datasets in LDMB format for faster IO speed. Please refer to wiki for more details.
Get Started
Please see wiki for the basic usage, i.e., training and testing.
Model Zoo and Baselines
Results and pre-trained models are available in the wiki-Model zoo.
Contributing
We appreciate all contributions. Please refer to mmdetection for contributing guideline.
Python code style

We adopt PEP8 as the preferred code style. We use flake8 as the linter and yapf as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the yapf configuration and flake8 configuration.
Before you create a PR, make sure that your code lints and is formatted by yapf.
Citation
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
@Article{tian2018tdan,
author = {Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title = {TDAN: Temporally deformable alignment network for video super-resolution},
journal = {arXiv preprint arXiv:1812.02898},
year = {2018},
}

About
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Resources
Readme
Apache-2.0 license
Activity
Stars
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Watchers
1 watching
Forks
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Report repository
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