Tensorflow implementation of Convolutional Neural Networks for super-resolution. The original Matlab and Caffe from official website can be found here.
- Tensorflow
- Scipy version > 0.18 ('mode' option from scipy.misc.imread function)
- h5py
- matplotlib
This code requires Tensorflow. Also scipy is used instead of Matlab or OpenCV. Especially, installing OpenCV at Linux is sort of complicated. So, with reproducing this paper, I used scipy instead. For more imformation about scipy, click here.
For training, python main.py
For testing, python main.py --is_train False --stride 21
After training 15,000 epochs, I got similar super-resolved image to reference paper. Training time takes 12 hours 16 minutes and 1.41 seconds. My desktop performance is Intel I7-6700 CPU, GTX970, and 16GB RAM. Result images are shown below.
Original butterfly image:
Bicubic interpolated image:
Super-resolved image:
- liliumao/Tensorflow-srcnn
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- I referred to this repository which is same implementation using Matlab code and Caffe model.
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- carpedm20/DCGAN-tensorflow
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- I have followed and learned training process and structure of this repository.
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