So, [Vidhu Joshi][2] and I experimented for weeks to make a Neural netowrk that could even remote address this issue. We used the famous [UNet][1] architecture (brilliant piece of work by the way) as our base netowrk to deblur the images. The network basically extracts the important features of the image while reducing its spacial features and then Up-Sampling those compact features aka recreating the original sized image but de-blurred.
It's been some time now that we've been working on the subject. As we went through the project, we kept recording all kinds of logs like hyper-parameters and their
corresponding results. We've managed to attain some good quality results(at very low computation costs) that can be improved drastically if given more attention.
We're planning to publish the work we've done so far and see where it takes us :D
We've tried to keep the directory structure and codebase as clean as possible. The utils directory contains a number of scripts written to
setup the entire project, like keras custom image generator classes, scripts to make the deblurred dataset, renaming files,etc
If anyone sees this of any interest for further research, please reach out to us anytime.
As we go along trying new stuff in new python notebooks, we keep adding them to the model_exps directory and name them try1 , try2 and so o.
At the top of each notebook is marked down specific parameters that were used in that particular experiment (eg. Epochs, depth of netowrk,
loss functions,etc) as well as the results fetched (eg. loss value). The data direcotry has not been uploaded due to our personal
reasons but its structure is as follows:
The best_models directory contains the all the saved models (in keras Saved Model format) that fetched results which were interested in
any way.
- First Image - Blurred
- Second Image - Clear
- Third Image - Predicted Clear
