Pytorch implement: Residual Dense Network for Image Super-Resolution

Two advantage ideas of the paper:

• join denese connect layer to ResNet
  
• concatenation of hierarchical features
  

Different with the paper, I just use there RDBs(Residual dense block), every RDB has three dense layers. So ,this is a sample implement the RDN(Residual Dense Network) proposed by the author.

• python3.5 / 3.6
• pytorch >= 0.2
• opencv

you need prepare DIV2K dataset (./data/)
train model :

python3 main.py --model_name 'RDN' --load demo_x3_RDN --dataDir ./DIV2K/ --need_patch True  --patchSize 144 --nDenselayer 3 --nFeat 64 --growthRate 32  --scale 3 --epoch 10000 --lrDecay 2000 --lr 1e-4 --batchSize 16 --nThreads 4 --lossType 'L1' 

densenet-pytorch

EDSR-PyTorch