Image super resolution

Introduction:

Related work:

A CSDN blog that describes details about what is super resolution
An excellent github, this repository was based on it, it offers me great help.
great job!!! strongly recommend

Requirements:

pytorch
numpy
h5py

Use

If you only want to use the model quickly, then directly jump to Step3

Step1: Prepare the dataset

download the standard dataset The 91-image(train set), Set5(test set) dataset converted to HDF5 can be downloaded from the links below.

Dataset	Scale	Type	Link
91-image	2	Train	Download
91-image	3	Train	Download
91-image	4	Train	Download
Set5	2	Eval	Download
Set5	3	Eval	Download
Set5	4	Eval	Download

Download any one of 91-image and Set5 in the same Scale and then move them under ./datasets as ./datasets/91-image_x2.h5 and ./datasets/Set5_x2.h5

Step2: Train the model

easy run
```
python train.py
```
about arguments
- train-file eval-file 2/3/4, different datasets to choose
- batch-size
- num-workers
- lr learning rate
- epoch
- f frequency to test the model
- model-dir where the model was saved
```
python train.py --train-file 4 --eval-file 4 --batch-size 64 --lr 1e-5 --num-workers 8 --epoch 500 --f 10 
```
All model will be saved under ./model and the best model is ./model/best.pth

Step3: Download the pretrained model(**If you have trained your model, skip this part)

trained by x2
trained by x4
Move the model weights under ./model as ./model/best.pth or ./model/b.pth

Step4: Do super resolution

python use.py --weights-file ./model/best.pth --image x/xx/xxx.jpg

By default, the weights file is ./model/best.pth, if you want to use b.pth please replace it. A picture will be created named as xxx_srcnn.jpg

Conclusion:

This is just a CNN try for me, to familiar with basic steps of machine learning, almost all code comes from here,I just follow his step and reapperance his job. The final result couldn't satisfy me, it actually should be called little super resolution haha. However, there's no doubt that idea of SRCNN was novelty in that period. If you want to improve the neural network, build a deeper model and try Resnet model.
If you are interested in it, you could see this great job and i strongly recommend it.

some errors you may occur

OSError: Unable to open file (file locking disabled on this file system (use HDF5_USE_FILE_LOCKING
Solution:
```
#linux
nano ~/.brashrc
```
Add export HDF5_USE_FILE_LOCKING='FALSE' in a line, use Ctrl-X to exit
```
source ~/.barshrc
```
OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "C:\ProgramData\Anaconda3\envs\study_and_test\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. Solution:
```
python train.py --num-workers 0
```

rideragito/image-super-resolution