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bpldenoising's Introduction

BPLDenoising

Set of experiments reported in the paper "Optimality Conditions and a Trust Region Algorithm for Bilevel Parameter Learning in Total Variation Image Denoising".

Installation

This module depends on Tuomo's Valkonen AlgTools and ImageTools packages as well as VariationalImaging and TestDatasets developed by David Villacís.

$ hg clone https://tuomov.iki.fi/repos/AlgTools/
$ hg clone https://tuomov.iki.fi/repos/ImageTools/

Once cloned the repositories, we need to upload those to the julia package manager

pkg> develop AlgTools
pkg> develop ImageTools
pkg> add https://github.com/dvillacis/VariationalImaging.git
pkg> add TestDatasets

To reproduce the experiments it is necessary to clone the code repository and initialize the julia modules

$ git clone https://github.com/dvillacis/BPLDenoising.git
$ julia --project=BPLDenoising

Once in the julia REPL just import the module and the experiment functions

julia> using BPLDenoising
julia> scalar_bilevel_tv_learn("dataset_name")

For the "dataset_name" variable you can choose one from the TestDatasets package.

bpldenoising's People

Contributors

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bpldenoising's Issues

Singular Matrix error

when solving patch sumregs bilevel learn, it raises a singular matrix when calculating the adjoint

Output path

Provide a way to the user to define the output directory

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