Revisiting Temporal Alignment for Video Restoration

Last update: Dec 25, 2022

Overview

Revisiting Temporal Alignment for Video Restoration [arXiv]

Kun Zhou, Wenbo Li, Liying Lu, Xiaoguang Han, Jiangbo Lu

We provide our results at Google Cloud.

We will also to update this repository with the testing/training code later.

Citing

If you find this code useful for your research, please consider citing the following paper:

@article{zhou2021rta,
title={Revisiting Temporal Alignment for Video Restoration},
author={Kun Zhou and Wenbo Li and Liying Lu and Xiaoguang Han and Jiangbo Lu},
journal={arXiv preprint arXiv:2111.15288},
year={2021}
}

Owner

GitHub Repository

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Revisiting Temporal Alignment for Video Restoration

Related tags

Overview

Revisiting Temporal Alignment for Video Restoration [arXiv]

Kun Zhou, Wenbo Li, Liying Lu, Xiaoguang Han, Jiangbo Lu

Citing

Owner

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

An official implementation of the paper Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

QTool: A Low-bit Quantization Toolbox for Deep Neural Networks in Computer Vision

Stream images from a connected camera over MQTT, view using Streamlit, record to file and sqlite

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How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.

Artifacts for paper "MMO: Meta Multi-Objectivization for Software Configuration Tuning"

The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.

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