Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing.

Ullah, H; Muhammad, K; Irfan, M; Anwar, S; Sajjad, M; Imran, AS; de Albuquerque, VHC

Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing.

Ullah, H Muhammad, K Irfan, M Anwar, S Sajjad, M Imran, AS de Albuquerque, VHC

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Trans Image Process, 2021, 30, pp. 8968-8982
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Ullah, H
dc.contributor.author	Muhammad, K
dc.contributor.author	Irfan, M
dc.contributor.author	Anwar, S
dc.contributor.author	Sajjad, M
dc.contributor.author	Imran, AS
dc.contributor.author	de Albuquerque, VHC
dc.date.accessioned	2022-05-08T22:08:54Z
dc.date.available	2022-05-08T22:08:54Z
dc.date.issued	2021
dc.identifier.citation	IEEE Trans Image Process, 2021, 30, pp. 8968-8982
dc.identifier.issn	1057-7149
dc.identifier.issn	1941-0042
dc.identifier.uri	http://hdl.handle.net/10453/157130
dc.description.abstract	Due to the rapid development of artificial intelligence technology, industrial sectors are revolutionizing in automation, reliability, and robustness, thereby significantly increasing quality and productivity. Most of the surveillance and industrial sectors are monitored by visual sensor networks capturing different surrounding environment images. However, during tempestuous weather conditions, the visual quality of the images is reduced due to contaminated suspended atmospheric particles that affect the overall surveillance systems. To tackle these challenges, this article presents a computationally efficient lightweight convolutional neural network referred to as Light-DehazeNet (LD-Net) for the reconstruction of hazy images. Unlike other learning-based approaches, which separately measure the transmission map and the atmospheric light, our proposed LD-Net jointly estimates both the transmission map and the atmospheric light using a transformed atmospheric scattering model. Furthermore, a color visibility restoration method is proposed to evade the color distortion in the dehaze image. Finally, we conduct extensive experiments using synthetic and natural hazy images. The quantitative and qualitative evaluation on different benchmark hazy datasets verify the superiority of the proposed method over other state-of-the-art image dehazing techniques. Moreover, additional experimentation validates the applicability of the proposed method in the object detection tasks. Considering the lightweight architecture with minimal computational cost, the proposed system is encouraged to be incorporated as an integral part of the vision-based monitoring systems to improve the overall performance.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Trans Image Process
dc.relation.isbasedon	10.1109/TIP.2021.3116790
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing.
dc.type	Journal Article
utslib.citation.volume	30
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-08T22:08:50Z
pubs.publication-status	Published
pubs.volume	30

Abstract:

Due to the rapid development of artificial intelligence technology, industrial sectors are revolutionizing in automation, reliability, and robustness, thereby significantly increasing quality and productivity. Most of the surveillance and industrial sectors are monitored by visual sensor networks capturing different surrounding environment images. However, during tempestuous weather conditions, the visual quality of the images is reduced due to contaminated suspended atmospheric particles that affect the overall surveillance systems. To tackle these challenges, this article presents a computationally efficient lightweight convolutional neural network referred to as Light-DehazeNet (LD-Net) for the reconstruction of hazy images. Unlike other learning-based approaches, which separately measure the transmission map and the atmospheric light, our proposed LD-Net jointly estimates both the transmission map and the atmospheric light using a transformed atmospheric scattering model. Furthermore, a color visibility restoration method is proposed to evade the color distortion in the dehaze image. Finally, we conduct extensive experiments using synthetic and natural hazy images. The quantitative and qualitative evaluation on different benchmark hazy datasets verify the superiority of the proposed method over other state-of-the-art image dehazing techniques. Moreover, additional experimentation validates the applicability of the proposed method in the object detection tasks. Considering the lightweight architecture with minimal computational cost, the proposed system is encouraged to be incorporated as an integral part of the vision-based monitoring systems to improve the overall performance.

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http://hdl.handle.net/10453/157130