SRCNN_Pytorch_1.0:pytorch1.0实现SRCNN

# SRCNN This repository is implementation of the ["Image Super-Resolution Using Deep Convolutional Networks"](https://arxiv.org/abs/1501.00092)(SRCNN)by PyTorch.  ## 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. [links:](https://pan.baidu.com/s/11XMR3rjXZuZHWX7yMeAqfw) Fetch Code：do34 Otherwise, you can use `prepare.py` to create custom dataset. ```bash python train.py --train-file "path_to_train_file" \ --eval-file "path_to_eval_file" \ --outputs-dir "path_to_outputs_file" \ --scale 3 \ --lr 1e-4 \ --batch-size 16 \ --num-epochs 400 \ --num-workers 0 \ --seed 123 ``` ## Test ```bash python test.py --weights-file "path_to_pth_file" \ --image-file "path_to_test_image_file" \ --scale 3 ``` ## Results We used the network settings for experiments, i.e., <a href="https://www.codecogs.com/eqnedit.php?latex={&space;f&space;}_{&space;1&space;}=9,{&space;f&space;}_{&space;2&space;}=5,{&space;f&space;}_{&space;3&space;}=5,{&space;n&space;}_{&space;1&space;}=64,{&space;n&space;}_{&space;2&space;}=32,{&space;n&space;}_{&space;3&space;}=1" target="_blank"><img src="https://latex.codecogs.com/gif.latex?{&space;f&space;}_{&space;1&space;}=9,{&space;f&space;}_{&space;2&space;}=5,{&space;f&space;}_{&space;3&space;}=5,{&space;n&space;}_{&space;1&space;}=64,{&space;n&space;}_{&space;2&space;}=32,{&space;n&space;}_{&space;3&space;}=1" title="{ f }_{ 1 }=9,{ f }_{ 2 }=5,{ f }_{ 3 }=5,{ n }_{ 1 }=64,{ n }_{ 2 }=32,{ n }_{ 3 }=1" /></a>. PSNR was calculated on the Y channel.