shovelingpig / SRCNN-pytorch

PyTorch implementation of Image Super-Resolution Using Deep Convolutional Networks (ECCV 2014)

SRCNN

This repository is implementation of the "Image Super-Resolution Using Deep Convolutional Networks".

Differences from the original

• Added the zero-padding
• Used the Adam instead of the SGD
• Removed the weights initialization

Requirements

• PyTorch 1.0.0
• Numpy 1.15.4
• Pillow 5.4.1
• h5py 2.8.0
• tqdm 4.30.0

Train

The 91-image, Set5 dataset converted to HDF5 can be downloaded from the links below.

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

Otherwise, you can use prepare.py to create custom dataset.

python train.py --train-file "BLAH_BLAH/91-image_x3.h5" \
                --eval-file "BLAH_BLAH/Set5_x3.h5" \
                --outputs-dir "BLAH_BLAH/outputs" \
                --scale 3 \
                --lr 1e-4 \
                --batch-size 16 \
                --num-epochs 400 \
                --num-workers 8 \
                --seed 123                

Test

Pre-trained weights can be downloaded from the links below.

Model	Scale	Link
9-5-5	2	Download
9-5-5	3	Download
9-5-5	4	Download

The results are stored in the same path as the query image.

python test.py --weights-file "BLAH_BLAH/srcnn_x3.pth" \
               --image-file "data/butterfly_GT.bmp" \
               --scale 3

Results

We used the network settings for experiments, i.e., .

PSNR was calculated on the Y channel.

Set5

Eval. Mat	Scale	SRCNN	SRCNN (Ours)
PSNR	2	36.66	36.65
PSNR	3	32.75	33.29
PSNR	4	30.49	30.25

Original	BICUBIC x3	SRCNN x3 (27.53 dB)
Original	BICUBIC x3	SRCNN x3 (29.30 dB)
Original	BICUBIC x3	SRCNN x3 (28.58 dB)