An Adaptive Correlated Image Prior for Image Restoration Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00490175" target="_blank" >RIV/67985556:_____/18:00490175 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/49777513:23220/18:43951319

Výsledek na webu

<a href="http://dx.doi.org/10.1109/LSP.2018.2836964" target="_blank" >http://dx.doi.org/10.1109/LSP.2018.2836964</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/LSP.2018.2836964" target="_blank" >10.1109/LSP.2018.2836964</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Adaptive Correlated Image Prior for Image Restoration Problems

Popis výsledku v původním jazyce

Image restoration is typically defined as an ill-posed problem which has to be regularized to obtain an acceptable solution. In Bayesian interpretation, regularization is equivalent to prior model of the image. An added value of Bayesian point of view is the ability to form a hierarchical model and estimate the hyper-parameters of the prior from the data. Many prior models are available, usually based on automatic relevance determination principle applied to the transformed image. However, the transformation (the most common is a differential operator) is assumed to be known. In this paper, we propose to relax this assumption and estimate the image transformation from the data. The resulting algorithm is analytically tractable using the Variational Bayes method. Properties of the new prior are demonstrated on the problem of image super-resolution.n

Název v anglickém jazyce

An Adaptive Correlated Image Prior for Image Restoration Problems

Popis výsledku anglicky

Image restoration is typically defined as an ill-posed problem which has to be regularized to obtain an acceptable solution. In Bayesian interpretation, regularization is equivalent to prior model of the image. An added value of Bayesian point of view is the ability to form a hierarchical model and estimate the hyper-parameters of the prior from the data. Many prior models are available, usually based on automatic relevance determination principle applied to the transformed image. However, the transformation (the most common is a differential operator) is assumed to be known. In this paper, we propose to relax this assumption and estimate the image transformation from the data. The resulting algorithm is analytically tractable using the Variational Bayes method. Properties of the new prior are demonstrated on the problem of image super-resolution.n

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Signal Processing Letters

ISSN

1070-9908

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

5

Strana od-do

1024-1028

Kód UT WoS článku

000434450200004

EID výsledku v databázi Scopus

2-s2.0-85047017461