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A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods
https://orcid.org/0000-0002-5115-4873
Abstract Images acquired in poor weather conditions (haze, fog, smog, mist, etc.) are often severely degraded. In the atmosphere, there exists two types of particles: dry particles (dust, smoke, etc.) and wet particles (water droplets, rain, etc.) Due to the scattering and absorption of these particles, various adverse effects, including reduced visibility and contrast, color distortions, etc. are introduced in the image. These degraded images are not acceptable for many computer vision applications such as smart transportation, video surveillance, weather forecasting, remote sensing, etc. The computer vision task associated with the mitigation of this effect is known as image dehazing. A high-quality input image (haze-free) is required to ensure the accurate working of these applications, supplied by image dehazing methods. The haze effect in the captured image is dependent on the distance from the observer to the scene. Besides, the scattering of particles adds non-linear and data-dependent noise to the captured image. Single image dehazing utilizes the physical model of hazy image formation in which estimation of depth or transmission is an important parameter to obtain a haze-free image. This review article groups the recent dehazing methods into different categories and elaborates the popular dehazing methods of each category. This category-wise analysis of different dehazing methods reveals that the deep learning and the restoration-based methods with priors have attracted the attention of the researchers in recent years in solving two challenging problems of image dehazing: dense haze and non-homogeneous haze. Also, recently, hardware implementation-based methods are introduced to assist smart transportation systems. This paper provides in-depth knowledge of this field; progress made to date and compares performance (both qualitative and quantitative) of the latest works. It covers a detailed description of dehazing methods, motivation, popular, and challenging datasets used for testing, metrics used for evaluation, and issues/challenges in this field from a new perspective. This paper will be useful to all types of researchers from novice to highly experienced in this field. It also suggests research gaps in this field where recent methods are lacking.
A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods
https://orcid.org/0000-0002-5115-4873
Abstract Images acquired in poor weather conditions (haze, fog, smog, mist, etc.) are often severely degraded. In the atmosphere, there exists two types of particles: dry particles (dust, smoke, etc.) and wet particles (water droplets, rain, etc.) Due to the scattering and absorption of these particles, various adverse effects, including reduced visibility and contrast, color distortions, etc. are introduced in the image. These degraded images are not acceptable for many computer vision applications such as smart transportation, video surveillance, weather forecasting, remote sensing, etc. The computer vision task associated with the mitigation of this effect is known as image dehazing. A high-quality input image (haze-free) is required to ensure the accurate working of these applications, supplied by image dehazing methods. The haze effect in the captured image is dependent on the distance from the observer to the scene. Besides, the scattering of particles adds non-linear and data-dependent noise to the captured image. Single image dehazing utilizes the physical model of hazy image formation in which estimation of depth or transmission is an important parameter to obtain a haze-free image. This review article groups the recent dehazing methods into different categories and elaborates the popular dehazing methods of each category. This category-wise analysis of different dehazing methods reveals that the deep learning and the restoration-based methods with priors have attracted the attention of the researchers in recent years in solving two challenging problems of image dehazing: dense haze and non-homogeneous haze. Also, recently, hardware implementation-based methods are introduced to assist smart transportation systems. This paper provides in-depth knowledge of this field; progress made to date and compares performance (both qualitative and quantitative) of the latest works. It covers a detailed description of dehazing methods, motivation, popular, and challenging datasets used for testing, metrics used for evaluation, and issues/challenges in this field from a new perspective. This paper will be useful to all types of researchers from novice to highly experienced in this field. It also suggests research gaps in this field where recent methods are lacking.
A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods
https://orcid.org/0000-0002-5115-4873
Abstract Images acquired in poor weather conditions (haze, fog, smog, mist, etc.) are often severely degraded. In the atmosphere, there exists two types of particles: dry particles (dust, smoke, etc.) and wet particles (water droplets, rain, etc.) Due to the scattering and absorption of these particles, various adverse effects, including reduced visibility and contrast, color distortions, etc. are introduced in the image. These degraded images are not acceptable for many computer vision applications such as smart transportation, video surveillance, weather forecasting, remote sensing, etc. The computer vision task associated with the mitigation of this effect is known as image dehazing. A high-quality input image (haze-free) is required to ensure the accurate working of these applications, supplied by image dehazing methods. The haze effect in the captured image is dependent on the distance from the observer to the scene. Besides, the scattering of particles adds non-linear and data-dependent noise to the captured image. Single image dehazing utilizes the physical model of hazy image formation in which estimation of depth or transmission is an important parameter to obtain a haze-free image. This review article groups the recent dehazing methods into different categories and elaborates the popular dehazing methods of each category. This category-wise analysis of different dehazing methods reveals that the deep learning and the restoration-based methods with priors have attracted the attention of the researchers in recent years in solving two challenging problems of image dehazing: dense haze and non-homogeneous haze. Also, recently, hardware implementation-based methods are introduced to assist smart transportation systems. This paper provides in-depth knowledge of this field; progress made to date and compares performance (both qualitative and quantitative) of the latest works. It covers a detailed description of dehazing methods, motivation, popular, and challenging datasets used for testing, metrics used for evaluation, and issues/challenges in this field from a new perspective. This paper will be useful to all types of researchers from novice to highly experienced in this field. It also suggests research gaps in this field where recent methods are lacking.
A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods
Agrawal, Subhash Chand (author) / Jalal, Anand Singh (author)
2022
Article (Journal)
Electronic Resource
English
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