Repository implementing Learnable Gradient Descent method for image restoration via recurrent neural networks (RNNs). Project is mainly based on this paper.
All requirements are stored in requirements.txt.
To install them, just run pip intall requirements.txt
All pretrained models can be downloaded from here. You can use them to run inference in corresponding Jupyter Notebooks
For convenience all inference experiments are given as corresponding Jupyter Notebooks:
| Path to notebook | Description |
|---|---|
| RNN_denoising.ipynb | Denoising, using learned gradient descent network |
| RNN_deblurring.ipynb | Deblurring, using learned gradient descent network |
| RNN_super-resolution.ipynb | Super-Resolution, using learned gradient descent network |
| TV_denoising_LBFGS.ipynb | Denoising, using total-variation restoration with L-BFGS minimizer |
| TV_deblurring_LBFGS.ipynb | Deblurring, using total-variation restoration with L-BFGS minimizer |
| TV_super-resolution_LBFGS.ipynb | Super-Resolution, using total-variation restoration with L-BFGS minimizer |
| TV_segsynthesis_LBFGS.ipynb | Semantic Synthesis, using total-variation restoration with L-BFGS minimizer (unsuccessful) |
BSD500 for training all linear problems
BSD68 for testing all linear problems
ADE20K for playing with semantic synthesis
This work is related to final project on 2020 Bayesian Methods of Machine Learning course at Skoltech. Initially I am trying to reproduce a restoration method, whcih is now commonly known as a learnable gradient descent. More detailed description is given in this document.
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