A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms

Shen, Minxian; Lv, Tianyi; Liu, Yi; Zhang, Jialiang; Ju, Mingye

doi:10.3390/electronics13173392

Open AccessArticle

A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms

by

Minxian Shen

^†

,

Tianyi Lv

^†

,

Yi Liu

,

Jialiang Zhang

and

Mingye Ju

^*

School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210046, China

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Electronics 2024, 13(17), 3392; https://doi.org/10.3390/electronics13173392

Submission received: 9 May 2024 / Revised: 15 August 2024 / Accepted: 19 August 2024 / Published: 26 August 2024

(This article belongs to the Special Issue Advanced Wireless Technologies for Next-G Networks: Antennas, Circuits, and Systems)

Download

Browse Figures

Versions Notes

Abstract

Images captured under adverse weather conditions often suffer from blurred textures and muted colors, which can impair the extraction of reliable information. Image defogging has emerged as a critical solution in computer vision to enhance the visual quality of such foggy images. However, there remains a lack of comprehensive studies that consolidate both traditional algorithm-based and deep learning-based defogging techniques. This paper presents a comprehensive survey of the currently proposed defogging techniques. Specifically, we first provide a fundamental classification of defogging methods: traditional techniques (including image enhancement approaches and physical-model-based defogging) and deep learning algorithms (such as network-based models and training strategy-based models). We then delve into a detailed discussion of each classification, introducing several representative image fog removal methods. Finally, we summarize their underlying principles, advantages, disadvantages, and give the prospects for future development.

Keywords: dehaze; image enhancement; traditional defogging algorithm; atmospheric scattering model; dark channel prior; CNN; unsupervised learning; deep learning

Share and Cite

MDPI and ACS Style

Shen, M.; Lv, T.; Liu, Y.; Zhang, J.; Ju, M. A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms. Electronics 2024, 13, 3392. https://doi.org/10.3390/electronics13173392

AMA Style

Shen M, Lv T, Liu Y, Zhang J, Ju M. A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms. Electronics. 2024; 13(17):3392. https://doi.org/10.3390/electronics13173392

Chicago/Turabian Style

Shen, Minxian, Tianyi Lv, Yi Liu, Jialiang Zhang, and Mingye Ju. 2024. "A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms" Electronics 13, no. 17: 3392. https://doi.org/10.3390/electronics13173392

APA Style

Shen, M., Lv, T., Liu, Y., Zhang, J., & Ju, M. (2024). A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms. Electronics, 13(17), 3392. https://doi.org/10.3390/electronics13173392

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI