图像增强参考代码.zip

# SRCNN This repository is implementation of the ["Image Super-Resolution Using Deep Convolutional Networks"](https://arxiv.org/abs/1501.00092). <center><img src="./thumbnails/fig1.png"></center> ## 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](https://www.dropbox.com/s/2hsah93sxgegsry/91-image_x2.h5?dl=0) | | 91-image | 3 | Train | [Download](https://www.dropbox.com/s/curldmdf11iqakd/91-image_x3.h5?dl=0) | | 91-image | 4 | Train | [Download](https://www.dropbox.com/s/22afykv4amfxeio/91-image_x4.h5?dl=0) | | Set5 | 2 | Eval | [Download](https://www.dropbox.com/s/r8qs6tp395hgh8g/Set5_x2.h5?dl=0) | | Set5 | 3 | Eval | [Download](https://www.dropbox.com/s/58ywjac4te3kbqq/Set5_x3.h5?dl=0) | | Set5 | 4 | Eval | [Download](https://www.dropbox.com/s/0rz86yn3nnrodlb/Set5_x4.h5?dl=0) | Otherwise, you can use `prepare.py` to create custom dataset. ```bash 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](https://www.dropbox.com/s/rxluu1y8ptjm4rn/srcnn_x2.pth?dl=0) | | 9-5-5 | 3 | [Download](https://www.dropbox.com/s/zn4fdobm2kw0c58/srcnn_x3.pth?dl=0) | | 9-5-5 | 4 | [Download](https://www.dropbox.com/s/pd5b2ketm0oamhj/srcnn_x4.pth?dl=0) | The results are stored in the same path as the query image. ```bash 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., <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. ### 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 | <table> <tr> <td><center>Original</center></td> <td><center>BICUBIC x3</center></td> <td><center>SRCNN x3 (27.53 dB)</center></td> </tr> <tr> <td> <center><img src="./data/butterfly_GT.bmp""></center> </td> <td> <center><img src="./data/butterfly_GT_bicubic_x3.bmp"></center> </td> <td> <center><img src="./data/butterfly_GT_srcnn_x3.bmp"></center> </td> </tr> <tr> <td><center>Original</center></td> <td><center>BICUBIC x3</center></td> <td><center>SRCNN x3 (29.30 dB)</center></td> </tr> <tr> <td> <center><img src="./data/zebra.bmp""></center> </td> <td> <center><img src="./data/zebra_bicubic_x3.bmp"></center> </td> <td> <center><img src="./data/zebra_srcnn_x3.bmp"></center> </td> </tr> <tr> <td><center>Original</center></td> <td><center>BICUBIC x3</center></td> <td><center>SRCNN x3 (28.58 dB)</center></td> </tr> <tr> <td> <center><img src="./data/ppt3.bmp""></center> </td> <td> <center><img src="./data/ppt3_bicubic_x3.bmp"></center> </td> <td> <center><img src="./data/ppt3_srcnn_x3.bmp"></center> </td> </tr> </table>