Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017060" target="_blank" >RIV/62690094:18450/20:50017060 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/9090867/" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9090867/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2020.2993607" target="_blank" >10.1109/ACCESS.2020.2993607</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions

Popis výsledku v původním jazyce

In recent years, the number of image fusion schemes presented by the research community has increased significantly. Measuring the performance of these schemes is an important issue. In this work, we introduce three quantitative fusion metrics to assess the quality of an image fusion algorithm. The proposed metrics rely on edge information that is obtained using fractional order differentiation. Edge and orientation strengths are fed into three sigmoidal functions separately for estimating the values of three normalized weighted metrics for the fused image corresponding to source images. The experiments on the multi-focus, infrared-visible and medical image fusion pairs demonstrate that the proposed fusion metrics are perceptually meaningful and outperform some of the state-of-the-art metrics.

Název v anglickém jazyce

Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions

Popis výsledku anglicky

In recent years, the number of image fusion schemes presented by the research community has increased significantly. Measuring the performance of these schemes is an important issue. In this work, we introduce three quantitative fusion metrics to assess the quality of an image fusion algorithm. The proposed metrics rely on edge information that is obtained using fractional order differentiation. Edge and orientation strengths are fed into three sigmoidal functions separately for estimating the values of three normalized weighted metrics for the fused image corresponding to source images. The experiments on the multi-focus, infrared-visible and medical image fusion pairs demonstrate that the proposed fusion metrics are perceptually meaningful and outperform some of the state-of-the-art metrics.

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

<a href="/cs/project/EF18_069%2F0010054" target="_blank" >EF18_069/0010054: IT4Neuro(degeneration)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

8

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

88385-88398

Kód UT WoS článku

000538766800004

EID výsledku v databázi Scopus

2-s2.0-85085204801