Single Image Dehazing via Deep Learning-based Image Restoration

Yeh, Chia‐Hung; Huang, Chih-Hsiang; Kang, Li‐Wei; Lin, Min-Hui

doi:10.23919/apsipa.2018.8659733

Cited by 17 publications

(3 citation statements)

References 23 publications

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“…The hazy images are divided into detail, and the base components are further improved; hazy and haze-free base parts are mapped 80 . These models are expected to incorporate more advanced architectures, such as attention mechanisms and generative adversarial networks, in order to enhance their performance further.…”

Section: Haze Removal Methodsmentioning

confidence: 99%

“…The haze-free reflectance can be defined as RðXÞ ¼ ðI r ðXÞ−NðXÞÞ LðXÞ after combining the modified illuminating and the outcome can be calculated as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 2 0 ; 1 1 4 ; 3 6 5 Îr ðXÞ ¼ RðXÞ • LðXÞ: (20) The hazy images are divided into detail, and the base components are further improved; hazy and haze-free base parts are mapped. 80 These models are expected to incorporate more advanced architectures, such as attention mechanisms and generative adversarial networks, in order to enhance their performance further.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

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Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.

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Section: Haze Removal Methodsmentioning

confidence: 99%

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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“…Yeh et al [8] proposed a deep CNN architecture for dehazing images through image restoration without mapping each pair of hazy images and its corresponding ground truth. The method outperformed other state-of-the-art dehazing algorithms; however, it is a time-consuming process to decompose an input hazy image and to extract detail components.…”

Section: Related Workmentioning

confidence: 99%

Single Image Dehazing: An Analysis on Generative Adversarial Network

Khatun¹,

Haque²,

Basri³

et al. 2020

JCC

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Haze is a very common phenomenon that degrades or reduces visibility. It causes various problems where high-quality images are required such as traffic and security monitoring. So haze removal from scenes is an immediate demand for clear vision. Recently, in addition to the conventional dehazing mechanisms, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance. But it is unclear how these algorithms would perform on hazy images acquired "in the wild" and how we could gauge the progress in the field. To bridge this gap, this presents a comprehensive study on three single image dehazing state-of-the-art GAN models, such as AOD-Net, cGAN, and DHSGAN. We have experimented using benchmark dataset consisting of both synthetic and real-world hazy images. The obtained results are evaluated both quantitatively and qualitatively. Among these techniques, the DHSGAN gives the best performance.

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Inception Network-Based Weather Image Classification with Pre-filtering Process

Feng

2019

Communications in Computer and Information Science

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Single Image Dehazing via Deep Learning-based Image Restoration

Cited by 17 publications

References 23 publications

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Single Image Dehazing: An Analysis on Generative Adversarial Network

Inception Network-Based Weather Image Classification with Pre-filtering Process

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