RDN

This repository is implementation of the "Residual Dense Network for Image Super-Resolution".

Requirements

• paddlepaddle 2.4.0
• paddleseg 2.8.0
• Numpy 1.15.4
• Pillow 5.4.1
• h5py 2.8.0
• tqdm 4.30.0

Train

The DIV2K, Set5 dataset converted to HDF5 can be downloaded from the links below.

Dataset	Scale	Type	Link
DIV2K	2	Train	Download
DIV2K	3	Train	Download
DIV2K	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.

prepare dataset

python prepare.py --images-dir "/root/autodl-tmp/paddle-FSRCNN/SR/WDSR/DIV2K/DIV2K_train_HR" \
                  --output-path "/root/autodl-tmp/paddle-FSRCNN/SR/DIV2K_X3.h5" \
                  --scale 3

train

python train.py --train-file "/root/autodl-tmp/paddle-FSRCNN/SR/DATA/DIV2K_X3.h5" \
                --eval-file "/root/autodl-tmp/paddle-FSRCNN/SR/RDN/BLAH_BLAH/Set5_x3 .h5" \
                --outputs-dir "BLAH_BLAH/outputs" \
                --scale 3 \
                --num-features 64 \
                --growth-rate 64 \
                --num-blocks 16 \
                --num-layers 8 \
                --lr 1e-4 \
                --batch-size 16 \
                --patch-size 32 \
                --num-epochs 800 \
                --num-workers 0 \
                --seed 123                

Test

Pre-trained weights can be downloaded from the links :链接：https://pan.baidu.com/s/17aVKmAG_k_Ag1Uiq1OCwjA?pwd=7nds 提取码：7nds

python test.py --weights-file "/root/autodl-tmp/paddle-FSRCNN/SR/RDN/BLAH_BLAH/outputs/x2/best.pdiparams" \
               --image-file "data/119082.png" \
               --scale 2 \
               --num-features 64 \
               --growth-rate 64 \
               --num-blocks 16 \
               --num-layers 8

Results

PSNR was calculated on the Y channel.