DRRN

This repository is implementation of the "Image Super-Resolution via Deep Recursive Residual Network".

Requirements

PyTorch 1.0.0
Numpy 1.15.4
Pillow 5.4.1
h5py 2.8.0
tqdm 4.30.0

Prepare

The images for creating a dataset used for training (291-image) or evaluation (Set5) can be downloaded from the paper author's implementation.

You can also use pre-created dataset files with same settings as the paper.

Dataset	Scale	Type	Link
291-image	2, 3, 4	Train	Download
Set5	2	Eval	Download
Set5	3	Eval	Download
Set5	4	Eval	Download

Generate training dataset

python generate_trainset.py --images-dir "BLAH_BLAH/Train_291" \
                            --output-path "BLAH_BLAH/Train_291_x234.h5" \
                            --patch-size 31 \
                            --stride 21

Generate test dataset

python generate_testset.py --images-dir "BLAH_BLAH/Set5" \
                           --output-path "BLAH_BLAH/Set5_x2.h5" \
                           --scale 2

Train

Model weights will be stored in the --outputs-dir after every epoch.

python train.py --train-file "BLAH_BLAH/Train_291_x234.h5" \
                --outputs-dir "BLAH_BLAH/DRRN_B1U9" \
                --B 1 \
                --U 9 \
                --num-features 128 \
                --lr 0.1 \
                --clip-grad 0.01 \
                --batch-size 128 \
                --num-epochs 50 \
                --num-workers 8 \
                --seed 123

You can also evaluate using --eval-file, --eval-scale options during training after every epoch. In addition, the best weights file will be stored in the --outputs-dir as a best.pth.

python train.py --train-file "BLAH_BLAH/Train_291_x234.h5" \
                --outputs-dir "BLAH_BLAH/DRRN_B1U9" \
                --eval-file "BLAH_BLAH/Set5_x2.h5" \
                --eval-scale 2 \
                --B 1 \
                --U 9 \
                --num-features 128 \
                --lr 0.1 \
                --clip-grad 0.01 \
                --batch-size 128 \
                --num-epochs 50 \
                --num-workers 8 \
                --seed 123

Evaluate

The pre-trained weights can be downloaded from the following links.

Model	Link
DRRN_B1U9	Download

python eval.py --weights-file "BLAH_BLAH/DRRN_B1U9/best.pth" \
               --eval-file "BLAH_BLAH/Set5_x2.h5" \
               --eval-scale 2 \
               --B 1 \
               --U 9 \
               --num-features 128

Results

The our model was learned and evaluated on the Y(luminance) channel.

For performance, we modified the original implementation as follows.

Batch normalization was removed from the residual unit.
No bias was used in the convolution layer.

Performance comparision on the Set5

Eval. Mat	Scale	DRRN_B1U9 (Paper)	DRRN_B1U9 (Ours)
PSNR	2	37.66	37.62
PSNR	3	33.93	33.86
PSNR	4	31.58	31.52

References

https://github.com/tyshiwo/DRRN_CVPR17

esjo93/DRRN-pytorch