Unofficial pytorch implementation of the paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution"

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Deep LearningDFSA
Overview

DFSA

Unofficial pytorch implementation of the ICCV 2021 paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution" (paper link: https://openaccess.thecvf.com/content/ICCV2021/papers/Magid_Dynamic_High-Pass_Filtering_and_Multi-Spectral_Attention_for_Image_Super-Resolution_ICCV_2021_paper.pdf)

The environmental settings are described below. (I cannot gaurantee if it works on other environments)

  • Pytorch=1.7.1+cu110
  • numpy=1.18.3
  • cv2=4.2.0
  • tqdm=4.45.0
  • lpips

Train

First, you need to download the DF2K dataset.

캡처

  • Set the database path in "./opt/option.py" (It is represented as "dir_data")

After those settings, you can run the train code by running "train.py"

  • python3 train.py --gpu_id 0 (execution code)
  • This code works on single GPU. If you want to train this code in muti-gpu, you need to change this code
  • Options are all included in "./opt/option.py". So you should change the variable in "./opt/option.py"

Inference (the code will be released later)

First, you need to specify variables in "./opt/option.py"

  • dir_test: root folder of test images
  • weights: checkpoint file (trained on DF2K dataset)
  • results: inference results will be saved on this folder

After those settings, you can run the inference code by running "inference.py"

  • python3 inference.py --gpu_id 0 (execution code)

Acknolwdgements

We refer to repos below to implement this code.

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