Image Super-Resolution with Deep Dictionary (ECCV 2022)

This repository provides the official PyTorch implementation of the following paper:
Shunta Maeda, "Image Super-Resolution with Deep Dictionary", ECCV 2022.

This repository is based on the official CutBlur repository.
Other than the addition of the proposed model model/srdd.py, changes were made only to solver.py and inference.py.

Dataset

We use the DIV2K dataset to train the model. Download and unpack the tar file any directory you want.
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.