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dhsr-rsrcnn-pytorch's Introduction

SRDH-Net & DHSR-Net

Introducion

"NOT COMPLETE YET"
This project is a pytorch implementation of merging "Super Resolution" and "Image Dehazing" into an End-to-End Network.
Reducing Computational Requirements of Image Dehazing Using Super-Resolution Networks

@INPROCEEDINGS{10219494,
  author={Weng, Shyang-En and Ye, Yan-Gu and Lin, Ying-Cheng and Miaou, Shaou-Gang},
  booktitle={2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)}, 
  title={Reducing Computational Requirements of Image Dehazing Using Super-Resolution Networks}, 
  year={2023},
  volume={},
  number={},
  pages={326-329},
  keywords={Training;Image quality;Interpolation;Computational modeling;Superresolution;Focusing;Computational efficiency;Image Dehazing;Super-Resolution;Advanced Driver Assistance Systems;Computational Requirement},
  doi={10.1109/IS3C57901.2023.00094}}

AOD-Net : All-in-One Network for Dehazing.

Preparation

Data Preparation

Train

python train_dhsr.py

You can change the parameter in train bash script to satisfied your project.

Test

python evaluate.py

Demo

This is the dehazing result image comparison. Left image is haze image, right image is clean image processed by DHSR-Net.

Credicts

AOD-Net : All-in-One Network for Dehazing. walsvid/AOD-Net-PyTorch
Image Super-Resolution Using Deep Convolutional Networks yjn870/SRCNN-pytorch

dhsr-rsrcnn-pytorch's People

Contributors

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Watchers

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