Image Super-Resolution Using Deep Convolutional Networks
Tensorflow implementation of SRCNN.
Prerequisites
- Python 3
- Tensorflow
- Numpy
- Scipy
- Opencv 3
- h5py
Usage
To train, uncomment the scripts in the bottom in net.py.
Then type python net.py
To test, set proper img_path, save_path and upscaling factor (multiplier) in the use_SRCNN.py.
Then type python use_SRCNN.py
Results
The following results are based on 45 hours of training on my i7 CPU.
We can also feed any image to this model to get an upscaled version with interpolated details:
Original image:
SRCNN:
Reference:
- Dong, C., Loy, C.C., He, K., Tang, X.: Learning a Deep Convolutional Network for Image Super-Resolution.
- tegg89/SRCNN-Tensorflow
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- I have followed the loading and storing of h5 format files of this repository.
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