SRCNN, Image Super-Resolution Using Deep Convolutional Networks in Tensorflow.

My implementation of Image Super-Resolution Using Deep Convolutional Networks in Tensorflow.

The algorithm is not mine, its an implementation of the SRCNN introduced by Dong et al

I have supplied a few functions to visulise the weights of the model (kernels) and the feature maps.

Project Structure:

• model - you can find the preon trained model, model is trained on a single colour channel, and hence expects this as as in input
• srcnn.py - here I initialise the network, and test it on image supplied in "image" directory
• outputs - self explanatory, you can check the results obtained in here

The project is using Python 3.5.

Network

• Convolitional Neural Network

• Filter Sizes: 9 - 1 - 5, as in the original paper

• Learning rate is 10^−4 for the first two layers, and 10^−5 for the last layer, according to the authors' benchmark

List of Dependencies:

• MatPlotLib - for visualisation

• NumPy - for everything, its NumPy

• Tensorflow - network built

• Scipy - for image processing

• Scikit-image - computing PSNR

NOT JUPYTER NOTEBOOK

Run from terminal, PyCharm, or from anything else that makes you happy:)

Everything else you need to know is in the comments.

Star, fork, do your thing.