Official repository of "BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment"

Overview

BasicVSR_PlusPlus (CVPR 2022)

[Paper] [Project Page] [Code]

This is the official repository for BasicVSR++. Please feel free to raise issue related to BasicVSR++! If you are also interested in RealBasicVSR, which is also accepted to CVPR 2022, please don't hesitate to star!

Authors: Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy, Nanyang Technological University

Acknowedgement: Our work is built upon MMEditing. Please follow and star this repository and MMEditing!

News

  • 2 Dec 2021: Colab demo released google colab logo
  • 18 Apr 2022: Code released. Also merged into MMEditing

TODO

  • Add data processing scripts
  • Add checkpoints for deblur and denoise
  • Add configs for deblur and denoise
  • Add Colab demo

Pre-trained Weights

You can find the pre-trained weights for deblurring and denoising in this link. For super-resolution and compressed video enhancement, please refer to MMEditing.

Installation

  1. Install PyTorch
  2. pip install openmim
  3. mim install mmcv-full
  4. git clone https://github.com/ckkelvinchan/BasicVSR_PlusPlus.git
  5. cd BasicVSR_PlusPlus
  6. pip install -v -e .

Inference a Video

  1. Download pre-trained weights
  2. python demo/restoration_video_demo.py ${CONFIG} ${CHKPT} ${IN_PATH} ${OUT_PATH}

For example, you can download the VSR checkpoint here to chkpts/basicvsr_plusplus_reds4.pth, then run

python demo/restoration_video_demo.py configs/basicvsr_plusplus_reds4.py chkpts/basicvsr_plusplus_reds4.pth data/demo_000 results/demo_000

You can also replace ${IN_PATH} ${OUT_PATH} by your video path (e.g., xxx/yyy.mp4) to input/output videos.

Training Models

  1. Put the dataset in the designated locations specified in the configuration file.
  2. sh tools/dist_train.sh ${CONFIG} ${NGPUS}

Data Preprocessing

To be added...

Related Work

Our BasicVSR series:

  1. BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, CVPR 2021
  2. Investigating Tradeoffs in Real-World Video Super-Resolution, CVPR 2022

More about deformable alignment:

Citations

@inproceedings{chan2022basicvsrpp,
  author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},
  title = {{BasicVSR++}: Improving video super-resolution with enhanced propagation and alignment},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
  year = {2022}
}
@article{chan2022generalization,
  title={On the Generalization of {BasicVSR++} to Video Deblurring and Denoising},
  author={Chan, Kelvin CK and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},
  journal={arXiv preprint arXiv:2204.05308},
  year={2022}
}
Owner
Kelvin C.K. Chan
Kelvin C.K. Chan
Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021)

Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021) This is the implementation of PSD (ICCV 2021),

12 Dec 12, 2022
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

416 Jan 09, 2023
Physics-informed Neural Operator for Learning Partial Differential Equation

PINO Physics-informed Neural Operator for Learning Partial Differential Equation Abstract: Machine learning methods have recently shown promise in sol

107 Jan 02, 2023
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS 2020) Introduction AdaShare is a novel and differentiable approach fo

94 Dec 22, 2022
Pmapper is a super-resolution and deconvolution toolkit for python 3.6+

pmapper pmapper is a super-resolution and deconvolution toolkit for python 3.6+. PMAP stands for Poisson Maximum A-Posteriori, a highly flexible and a

NASA Jet Propulsion Laboratory 8 Nov 06, 2022
Repository of best practices for deep learning in Julia, inspired by fastai

FastAI Docs: Stable | Dev FastAI.jl is inspired by fastai, and is a repository of best practices for deep learning in Julia. Its goal is to easily ena

FluxML 532 Jan 02, 2023
Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation

Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation Overview This example will show how to validate the status of our firewall before and a

Calvin Remsburg 1 Jan 07, 2022
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

HierarchicyBandit Introduction This is the implementation of WSDM 2022 paper : Show Me the Whole World: Towards Entire Item Space Exploration for Inte

yu song 5 Sep 09, 2022
8-week curriculum for AI Builders

curriculum 8-week curriculum for AI Builders สารบัญ บทที่ 1 - Machine Learning คืออะไร บทที่ 2 - ชุดข้อมูลมหัศจรรย์และถิ่นที่อยู่ บทที่ 3 - Stochastic

AI Builders 134 Jan 03, 2023
SegNet-Basic with Keras

SegNet-Basic: What is Segnet? Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-wise Image Segmentation Segnet = (Encoder + Decoder)

Yad Konrad 81 Jun 30, 2022
PyTorch implementation for "Mining Latent Structures with Contrastive Modality Fusion for Multimedia Recommendation"

MIRCO PyTorch implementation for paper: Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation Dependencies Python 3.

Big Data and Multi-modal Computing Group, CRIPAC 9 Dec 08, 2022
This is an official pytorch implementation of Lite-HRNet: A Lightweight High-Resolution Network.

Lite-HRNet: A Lightweight High-Resolution Network Introduction This is an official pytorch implementation of Lite-HRNet: A Lightweight High-Resolution

HRNet 675 Dec 25, 2022
Pytorch-3dunet - 3D U-Net model for volumetric semantic segmentation written in pytorch

pytorch-3dunet PyTorch implementation 3D U-Net and its variants: Standard 3D U-Net based on 3D U-Net: Learning Dense Volumetric Segmentation from Spar

Adrian Wolny 1.3k Dec 28, 2022
A Simple Key-Value Data-store written in Python

mercury-db This is a File Based Key-Value Datastore that supports basic CRUD (Create, Read, Update, Delete) operations developed using Python. The dat

Vaidhyanathan S M 1 Jan 09, 2022
Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Mo

Abhinav Kumar 76 Jan 02, 2023
Official Pytorch implementation for "End2End Occluded Face Recognition by Masking Corrupted Features, TPAMI 2021"

End2End Occluded Face Recognition by Masking Corrupted Features This is the Pytorch implementation of our TPAMI 2021 paper End2End Occluded Face Recog

Haibo Qiu 25 Oct 31, 2022
No Code AI/ML platform

NoCodeAIML No Code AI/ML platform - Community Edition Video credits: Uday Kiran Typical No Code AI/ML Platform will have features like drag and drop,

Bhagvan Kommadi 5 Jan 28, 2022
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting (RVM) English | 中文 Official repository for the paper Robust High-Resolution Video Matting with Temporal Guidance. RVM is specific

flow-dev 2 Aug 21, 2022
Code for "Localization with Sampling-Argmax", NeurIPS 2021

Localization with Sampling-Argmax [Paper] [arXiv] [Project Page] Localization with Sampling-Argmax Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-

JeffLi 71 Dec 17, 2022
An implementation of a sequence to sequence neural network using an encoder-decoder

Keras implementation of a sequence to sequence model for time series prediction using an encoder-decoder architecture. I created this post to share a

Luke Tonin 195 Dec 17, 2022