Single Image Dehazing via Fusion of Multilevel Attention Network for Vision-Based Measurement Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020437" target="_blank" >RIV/62690094:18450/23:50020437 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10115496" target="_blank" >https://ieeexplore.ieee.org/document/10115496</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIM.2023.3271753" target="_blank" >10.1109/TIM.2023.3271753</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Single Image Dehazing via Fusion of Multilevel Attention Network for Vision-Based Measurement Applications

Popis výsledku v původním jazyce

Nowadays, researchers use vision-based measurement tools to record, detect, and monitor an atmospheric phenomenon called haze. It impedes the proper functioning of many outdoor industrial systems, such as autonomous driving, surveillance, satellite imagery, etc. Conventional visibility restoration methods cannot accurately recover image quality due to inaccurate estimations of haze thickness and the presence of color-cast effects. Deep neural networks are evolving due to their ability to directly dehaze images from hazy scenes. Therefore, a unique attention-based end-to-end dehazing network named Oval-Net has been proposed in this study to restore clear images from its counterpart without employing the atmospheric scattering model. The Oval-Net is an encoder-decoder architecture that uses spatial and channel attention at each stage to focus on dominant and significant information while avoiding the transmission of irrelevant information from the encoder to the decoder, allowing quicker convergence. The proposed approach outperforms seven state-of-the-art algorithms in quantitative and qualitative assessments of a variety of synthetic and real-world hazy images, proving its effectiveness for vision-based industrial systems. IEEE

Název v anglickém jazyce

Single Image Dehazing via Fusion of Multilevel Attention Network for Vision-Based Measurement Applications

Popis výsledku anglicky

Nowadays, researchers use vision-based measurement tools to record, detect, and monitor an atmospheric phenomenon called haze. It impedes the proper functioning of many outdoor industrial systems, such as autonomous driving, surveillance, satellite imagery, etc. Conventional visibility restoration methods cannot accurately recover image quality due to inaccurate estimations of haze thickness and the presence of color-cast effects. Deep neural networks are evolving due to their ability to directly dehaze images from hazy scenes. Therefore, a unique attention-based end-to-end dehazing network named Oval-Net has been proposed in this study to restore clear images from its counterpart without employing the atmospheric scattering model. The Oval-Net is an encoder-decoder architecture that uses spatial and channel attention at each stage to focus on dominant and significant information while avoiding the transmission of irrelevant information from the encoder to the decoder, allowing quicker convergence. The proposed approach outperforms seven state-of-the-art algorithms in quantitative and qualitative assessments of a variety of synthetic and real-world hazy images, proving its effectiveness for vision-based industrial systems. IEEE

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Instrumentation and Measurement

ISSN

0018-9456

e-ISSN

1557-9662

Svazek periodika

72

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

"Article number: 4503415"

Kód UT WoS článku

000991806800045

EID výsledku v databázi Scopus

2-s2.0-85159803427