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PhD Thesis Code

This code supports the PhD thesis "Total Variation Bilevel Learning: Optimality Conditions and Numerical Solution" that can be found here.

Installation

$ conda create -n bilevel
$ conda activate bilevel
$ conda install numpy pillow scikit-image
$ pip install pylops pyproximal

Learning Data Parameter

Scalar

$ conda activate bilevel
$ python data_learning.py datasets/cameraman_128_5/filelist.txt -t scalar -i 15

Patch

$ conda activate bilevel
$ python data_learning.py datasets/cameraman_128_5/filelist.txt -t patch -prows 2 -pdata 15 15 15 15

Learning Regularization Parameter

Scalar

$ conda activate bilevel
$ python regularization_learning.py datasets/cameraman_128_5/filelist.txt -t scalar -i 0.1

Patch

$ conda activate bilevel
$ python regularization_learning.py datasets/cameraman_128_5/filelist.txt -t patch -prows 2 -pdata 0.1 0.1 0.1 0.1

Optional Parameters

  1. -o/--output: Directory where to store the results
  2. -v/--verbose: Print evolution of iterations of the bilevel solver
  3. -i/--init: Initial value for the parameter
  4. -ps/--patch_size: Patch size of the parameter
  5. -g/--gamma: Smoothing parameter for the TV norm

Example

$ python data_learning.py datasets/cameraman_128_5/filelist.txt -t scalar -i 10 -o output/cameraman_128_5/10/ -v

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