Awesome
Image Super-Resolution with Deep Dictionary (ECCV 2022)
This repository provides the official PyTorch implementation of the following paper:<br> Shunta Maeda, "Image Super-Resolution with Deep Dictionary", ECCV 2022.<br>
This repository is based on the official CutBlur repository.<br>
Other than the addition of the proposed model model/srdd.py
, changes were made only to solver.py
and inference.py
.<br>
Dataset
We use the DIV2K dataset to train the model. Download and unpack the tar file any directory you want.<br>
Important: For the DIV2K dataset only, all the train and valid images should be placed in the DIV2K_train_HR
and DIV2K_train_LR_bicubic
directories (We parse train and valid images using --div2k_range
argument).
Train
python main.py \
--model SRDD \
--dataset DIV2K_SR \
--div2k_range 1-800/801-810 \
--scale 4 \
--dataset_root <directory_of_dataset> \
--save_result \
--patch_size 48 \
--batch_size 32 \
--lr 2e-4 \
--decay "200-300-350-375" \
--max_steps 400000
Test
python inference.py \
--model SRDD \
--scale 4 \
--pretrain <path_of_pretrained_model> \
--dataset_root ./input \
--save_root ./output
Updates
- 14 July, 2022: Initial upload.
Acknowledgements
This code is built on CutBlur. We thank the authors for sharing their codes.