Research progress and applications of image defogging algorithms-ZENTIME PUBLISHING CORPORATION LIMITED

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Research progress and applications of image defogging algorithms

Yi Chen^1,*, Shiju Yan^1,*, Yunhua Xu¹, Linping Gu²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. ²Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China.

*The authors contribute equally.

Address correspondence to: Shiju Yan, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. Tel: 18956153985, E-mail: yanshiju@usst.edu.cn.

Received April 25, 2023; Accepted August 28, 2023; Published September 30, 2023

DOI: https://doi.org/10.61189/145362zgyopx

Highlights

● The mainstream defogging algorithms can be classified into three categories based on their principles: image enhancement-based, physical model-based, and neural network-based.

● This paper aims to introduce and explore these categories, as well as to provide an outlook on the application and possible future development directions of defogging algorithms.

Abstract

Images taken under hazy weather conditions suffer from problems such as blurring, low contrast, and low saturation due to the scattering of atmospheric light by aerosol particles in the air, which affects the performance and judgment of image analysis equipment. With the rapid development of image processing technology and computer vision technology, researchers have proposed a large number of targeted haze removal algorithms to improve the quality of images taken under hazy weather conditions. According to the haze removal principle, mainstream haze removal algorithms can be classified into three categories: image enhancement-based, physics model-based, and neural network-based. This paper introduces and explores classic haze removal algorithms from the perspectives of principles, development, advantages, and disadvantages, and outlines the prospects for the future development and application direction of haze removal algorithms.

Keywords: Haze removal algorithm, image enhancement, physics model, neural network.

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