Trends and Prospects of Techniques for Haze Removal From Degraded Images: A Survey

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019187" target="_blank" >RIV/62690094:18450/22:50019187 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9779097" target="_blank" >https://ieeexplore.ieee.org/document/9779097</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TETCI.2022.3173443" target="_blank" >10.1109/TETCI.2022.3173443</a>

Result language

angličtina

Original language name

Trends and Prospects of Techniques for Haze Removal From Degraded Images: A Survey

Original language description

For the last two decades, image processing techniques have been used frequently in computer vision applications. The most challenging task in image processing is restoring images that are degraded due to various weather conditions. Mainly, the visibility of outdoor images is corrupted due to adverse atmospheric effects. The visibility of acquired images is reduced in these circumstances. Haze is an atmospheric phenomenon that reduces the clarity of an image. Due to the presence of particles such as dust, dirt, soot, or smoke, there is significant decay in the color and contrast of captured images. Haze present in acquired images causes issues in a variety of computer vision applications. Therefore, enhancing the contrast of a hazy image and restoring the visibility of the scene is essential. Since clear images are required in every application, image dehazing is an important step. Hence, many researchers are working on it. Different methods have been presented in the literature for image dehazing. This study describes various traditional and deep learning techniques of image dehazing from an analytical perspective. The main intention behind this work is to provide an intuitive understanding of the major techniques that have made a relevant contribution to haze removal. In this paper, we have covered different types of contributions toward dehazing based on the traditional method as well as deep learning approaches. With a considerable amount of instinctive simplifications, the reader is expected to have an improved ability to visualize the internal dynamics of these processes. IEEE

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

IEEE Transactions on Emerging Topics in Computational Intelligence

ISSN

2471-285X

e-ISSN

—

Volume of the periodical

6

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

762-782

UT code for WoS article

000799567800001

EID of the result in the Scopus database

2-s2.0-85130505606