Bounded variation spaces with generalized Orlicz growth related to image denoising

Fuente: arXiv
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Autori principali: Eleuteri, Michela, Harjulehto, Petteri, Hästö, Peter
Natura: Preprint
Pubblicazione: 2022
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author Eleuteri, Michela
Harjulehto, Petteri
Hästö, Peter
author_facet Eleuteri, Michela
Harjulehto, Petteri
Hästö, Peter
contents Motivated by the image denoising problem and the undesirable stair-casing effect of the total variation method, we introduce bounded variation spaces with generalized Orlicz growth. Our setup covers earlier variable exponent and double phase models. We study the norm and modular of the new space and derive a formula for the modular in terms of the Lebesgue decomposition of the derivative measure and a location dependent recession function. We also show that the modular can be obtained as the $Γ$-limit of uniformly convex approximating energies.
format Preprint
id arxiv_https___arxiv_org_abs_2211_15256
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Bounded variation spaces with generalized Orlicz growth related to image denoising
Eleuteri, Michela
Harjulehto, Petteri
Hästö, Peter
Functional Analysis
35J60, 26B30, 35B40, 35J25, 46E35, 49J27, 49J45
Motivated by the image denoising problem and the undesirable stair-casing effect of the total variation method, we introduce bounded variation spaces with generalized Orlicz growth. Our setup covers earlier variable exponent and double phase models. We study the norm and modular of the new space and derive a formula for the modular in terms of the Lebesgue decomposition of the derivative measure and a location dependent recession function. We also show that the modular can be obtained as the $Γ$-limit of uniformly convex approximating energies.
title Bounded variation spaces with generalized Orlicz growth related to image denoising
topic Functional Analysis
35J60, 26B30, 35B40, 35J25, 46E35, 49J27, 49J45
url https://arxiv.org/abs/2211.15256