wwhio / MSDTGP

Satellite Video Super-resolution via Multi-Scale Deformable Convolution Alignment and Temporal Grouping Projection, IEEE TGRS, 2021

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Satellite Video Super-resolution via Multi-Scale Deformable Convolution Alignment and Temporal Grouping Projection (TGRS)

Introuction

This is the official implementation of our paper Satellite Video Super-resolution via Multi-Scale Deformable Convolution Alignment and Temporal Grouping Projection published on IEEE Transactions on Geoscience and Remote Sensing (TGRS).

The network structure

image

Quantitive results

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Qualitive results

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More details can be found in our paper!

Environment

  • CUDA 10.0
  • pytorch 1.x
  • build DCNv2

Dataset Preparation

Please download our dataset Jilin-189 Code:31ct
You can also train your dataset following the directory sturture below!

Data directory structure

trainset--
 | train--
  | LR4x---
   | 000.png
   | ···.png
   | 099.png
  | GT---
  | Bicubic4x---

testset--
 | eval--
  | LR4x---
   | 000.png
   | ···.png
   | 099.png
  | GT---
  | Bicubic4x---

Training

python main.py

Test

python eval.py

Citation

If you find our work helpful, please cite:

@ARTICLE{9530280,  
author={Xiao, Yi and Su, Xin and Yuan, Qiangqiang and Liu, Denghong and Shen, Huanfeng and Zhang, Liangpei},  
journal={IEEE Transactions on Geoscience and Remote Sensing},  
title={Satellite Video Super-Resolution via Multiscale Deformable Convolution Alignment and Temporal Grouping Projection},   
year={2021},  
volume={60},  
number={},  
pages={1-19},  
doi={10.1109/TGRS.2021.3107352}
}

Acknowledgement

Our work is built upon RBPN and TDAN.
Thanks to the author for the source code !

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About

Satellite Video Super-resolution via Multi-Scale Deformable Convolution Alignment and Temporal Grouping Projection, IEEE TGRS, 2021


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Language:Python 45.7%Language:C++ 27.0%Language:Cuda 22.7%Language:C 4.6%Language:Shell 0.0%