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dc.contributor.authorUllah, Hayat
dc.contributor.authorMuhammad, Khan
dc.contributor.authorIrfan, Muhammad
dc.contributor.authorAnwar, Saeed
dc.contributor.authorSajjad, Muhammad
dc.contributor.authorImran, Ali Shariq
dc.contributor.authorAlbuquerque, Victor Hugo C. de
dc.date.accessioned2022-10-05T06:50:40Z
dc.date.available2022-10-05T06:50:40Z
dc.date.created2021-10-11T13:08:08Z
dc.date.issued2021
dc.identifier.citationIEEE Transactions on Image Processing. 2021, 30 8968-8982.en_US
dc.identifier.issn1057-7149
dc.identifier.urihttps://hdl.handle.net/11250/3023850
dc.description.abstractDue 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.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleLight-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazingen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holderThis version of the article will not be available due to copyright restrictions by IEEEen_US
dc.source.pagenumber8968-8982en_US
dc.source.volume30en_US
dc.source.journalIEEE Transactions on Image Processingen_US
dc.identifier.doi10.1109/TIP.2021.3116790
dc.identifier.cristin1944900
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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