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Tensorflow implementation of single image super-resolution using a Convolutional Neural Network

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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.

Bicubic interpolation:
bicubic
SRCNN:
srcnn



Bicubic interpolation:
bicubic
SRCNN:
srcnn



Bicubic interpolation:
bicubic
SRCNN:
srcnn

We can also feed any image to this model to get an upscaled version with interpolated details:
Original image:
lenna
SRCNN:
3xlenna

Reference:

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Tensorflow implementation of single image super-resolution using a Convolutional Neural Network

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