Numpy

Numpy implementation

This is an implementation of the Super Resolution Convolutional Neural Network (SRCNN) by Dong, Chao, et al. "Image super-resolution using deep convolutional networks." (https://arxiv.org/abs/1501.00092v3) done in Python utilising Numpy.

Prerequisites

• Python (tested on Python 3.8)
  • Numpy (tested on 1.20.1)
  • Pillow (tested on 8.1.0)
  • sewar (tested on 1.0.12)

Limitations

This implementation is very inefficient (read: slow). If you are using a PC it is recommended to use the torch implementation in this repository. If you are using Avnet's ZedBoard or a Linux-based system and don't want to install torch it is recommended to use the Cython implementation in this repository.

Usage

1. If you are not there already, change into the repository and the Numpy folder with cd ~/SRCNN/Numpy/

2. There are a few test images in ~/SRCNN/Images/ like bird.bmp or butterfly.bmp

3. execute the script with

   python3.8 test_numpy.py --image-file "[Path to image file]"

   e.g. python3.8 test_numpy.py --image-file "../Images/butterfly.bmp"

4. In the location of the original image there should be three additional images. Example with butterfly.bmp:

   1. butterfly_GT.bmp: original image (Ground truth)
   2. butterfly_bicubic_x3.bmp: image with bicubic upscaling
   3. butterfly_srcnn_x3.bmp: image with SRCNN upscaling