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SuperHighResProject

Repository of "Learning for super resolution" project 2018

The purpose of this project was to compare two approaches to deep learning based super resolution, one based on wavelet transforms and the other on the spatial domain. Models were compared with respect to each other as well as to bicubic interpolation through measures such as PSNR, SSIM and RMSE. It was shown that networks trained on the frequency and spatial domain outperformed bicubic interpolation and the two had very similar performance with wavelets achieving a slightly higher performance.

We trainned two networks in wavelet and spatial domain using residual netkorks and keras.

All image processing methods are in the file "srPreprocessing.py" Networks architecture is implemented in srcnn.py and wavelet_cnn.py in spatial and wavelets domain respectively

Two notebooks for each model pipeline from the high definition image, trainning the models to predicting results

Preprocessing and Netbook training in wavelet domain pipeline :

SRCNN_notebook.ipynb

Preprocessing and Netbook training in spatial domain pipeline :

  SRCNN_spatial_notebook.ipynb

Notebook with all metrics used to compare, examples, and results:

Comparison.ipynb

Motivations, discussion and results are "report" folder

References

Learning a Deep Convolutional Network for Image Super-Resolution, Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang

Accurate Image Super-Resolution Using Very Deep Convolutional Networks Jiwon Kim, Jung Kwon Lee and Kyoung Mu Lee

Deep Wavelet Prediction for Image Super-resolution Tiantong Guo, Hojjat Seyed Mousavi, Tiep Huu Vu, Vishal Monga

J. Simpkins, R.L. Stevenson, "An Introduction to Super-Resolution Imaging." Mathematical Optics: Classical, Quantum, and Computational Methods, Ed. V. Lakshminarayanan, M. Calvo, and T. Alieva. CRC Press, 2012. 539-564.

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