Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICCV_2017/papers/Tong_Image_Super-Resolution_Using_ICCV_2017_paper.pdf) 
usage: main.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR] [--step STEP] [--cuda] [--resume RESUME] [--start-epoch START_EPOCH] [--threads THREADS] [--pretrained PRETRAINED] Pytorch SRDenseNet train optional arguments: -h, --help show this help message and exit --batchSize BATCHSIZE training batch size --nEpochs NEPOCHS number of epochs to train for --lr LR Learning Rate. Default=1e-4 --step STEP Sets the learning rate to the initial LR decayed by 10 every n epochs, Default: n=30 --cuda Use cuda? --resume RESUME Path to checkpoint (default: none) --start-epoch START_EPOCH Manual epoch number (useful on restarts) --threads THREADS Number of threads for data loader to use, Default: 1 --pretrained PRETRAINED path to pretrained model (default: none) usage: test.py [-h] [--cuda] [--model MODEL] [--imageset IMAGESET] [--scale SCALE] Pytorch SRDenseNet Test optional arguments: -h, --help show this help message and exit --cuda use cuda? --model MODEL model path --imageset IMAGESET imageset name --scale SCALE scale factor, Default: 4 The training data is generated with Matlab Bicubic Interplotation, please refer Code for Data Generation for creating training files.
The test imageset is generated with Matlab Bicubic Interplotation, please refer Code for test for creating test imageset.
We provide a pretrained .SRDenseNet x4 model trained on DIV2K images from [DIV2K_train_HR] (http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip).While I use the SR_DenseNet to train this model, so the performance is test based on this code.
Non-overlapping sub-images with a size of 96 × 96 were cropped in the HR space. Other settings is the same as the original paper
- Performance in PSNR on Set5, Set14, and BSD100
| DataSet/Method | Paper | PyTorch |
|---|---|---|
| Set5 | 32.02/0.893 | 31.57/0.883 |
| Set14 | 28.50/0.778 | 28.11/0.771 |
| BSD100 | 27.53/0.733 | 27.32/0.729 |