Deep Convolution Modulation for Image Super-resolution (TCSVT 2023)

This repository is for CoMoNet introduced in the following paper

Yuanfei Huang, Jie Li, Yanting Hu, Hua Huang and Xinbo Gao, "Deep Convolution Modulation for Image Super-resolution", IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 5, pp. 3647-3662, 2024. paper

Overflow

Dependenices

• python 3.8
• pytorch >= 1.7.0
• NVIDIA GPU + CUDA

Data preparing

Download DIV2K datasets into the path "../../Datasets/Train/DIV2K".

Train

1. Replace the train dataset path '../../Datasets/Train/' and validation dataset '../../Datasets/Test/' with your training and validation datasets, respectively.

2. Set the configurations in 'option.py' as you want.

python main.py --train 'Train'

Test

1. Download models from 'models/'.

2. Replace the test dataset path '../../Datasets/Test/' with your datasets.

python main.py --train 'Test'

Results

Citation

@ARTICLE{10256095,
  author={Huang, Yuanfei and Li, Jie and Hu, Yanting and Huang, Hua and Gao, Xinbo},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Deep Convolution Modulation for Image Super-Resolution}, 
  year={2024},
  volume={34},
  number={5},
  pages={3647-3662}