Deep Convolution Modulation for Image Super-resolution

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", submitted.

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