Bilevel Optimization Methods in Imaging

Juan Carlos De los Reyes () and David Villacís ()
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Juan Carlos De los Reyes: Escuela Politécnica Nacional, Research Center for Mathematical Modelling (MODEMAT)
David Villacís: Escuela Politécnica Nacional, Research Center for Mathematical Modelling (MODEMAT)

Chapter 24 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 909-941 from Springer

Abstract: Abstract Optimization techniques have been widely used for image restoration tasks, as many imaging problems may be formulated as minimization ones with the recovered image as the target minimizer. Recently, novel optimization ideas also entered the scene in combination with machine learning approaches, to improve the reconstruction of images by optimally choosing different parameters/functions of interest in the models. This chapter provides a review of the latest developments concerning the latter, with special emphasis on bilevel optimization techniques and their use for learning local and nonlocal image restoration models in a supervised manner. Moreover, the use of related optimization ideas within the development of neural networks in imaging will be briefly discussed.

Keywords: Bilevel optimization; Machine learning; Variational models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98661-2_66

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DOI: 10.1007/978-3-030-98661-2_66

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