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inzva AI Projects #2 - Image Restoration Project
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Image-Restoration Team

Contributors (All equally contributed):

  • Ahmet Melek
  • Onur Boyar
  • Furkan Gürsoy
  • Burak Satar

We restore very dark images to high quality and visible images.

Here is an example from the reference paper: Here is an example from the reference paper

Our purposes on this project are:

1- Reproduce the results of Learning to See in the Dark project, as can be seen here:

2- Obtain results faster via optimization of the code.

3- Trying to have better results with modifications. (optional goal)

4- Testing different architectures on this problem. (optional goal)

inzva AI Projects #2 - Image Restoration Project


1- Paths and hyperparameters can be set at the top of and files.

2- The files will be read from respective input and ground truth directories.

3- The size of the deep neural network will be decided based on hyperparameters.

4- Training and test sets are generated based on the first characters of the filenames. Please refer to the code for specific implementation.

5- Output images and trained models will be saved in result and checkpoint directories.

6- For both training and test; epoch, loss, time information are printed during execution.

Let us know if you spot any error or have any suggestions.

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