Image super-resolution

starter code

In this project we will implement an image super-resolution network. The goal of this project is to take an input image of size 64 x 64 and produce one super-resolved image of size 256 x 256. You may use any traning data and algorithm for this tasks. However, you should choose one of four different loss functions:

We provide a python starter code which contains a project and a val_grader. Please implement your superresolution network in one function, as the grader will load your model super-resolve the test images. The starter code also contains a downsample function that we use to produce the low-resolution images. During testing we will evaluate your algorithm on three criteria:

Not all losses above will do equally well in all criteria. We only require you to reach 60pt for a full score. If you’re complementation does not reach 60pt, please mention in a writeup why. A good explaination why, will also give you full credit.

We will again use a hidden dataset to measure the performance of your model.

Please load your model once when your module starts up and not every time your model super-resolves.

The best two performing models per loss (paper) will receive extra credit and should be prepared to brifly explain their solution in class.

You may use anyones auto-encoder code from last assignment.