A novel image dehazing framework for robust vision‐based intelligent systems

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE A novel image dehazing framework for robust vision-based intelligent systems Farah Deeba, Farah Deeba orcid.org/0000-0002-1987-2402 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorFayaz A. Dharejo, Fayaz A. Dharejo orcid.org/0000-0001-7685-3913 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorMuhammad Zawish, Muhammad Zawish orcid.org/0000-0002-4994-3443 Telecommunication Software and Systems Group, Waterford Institute of Technology, Waterford, IrelandSearch for more papers by this authorFida H. Memon, Fida H. Memon orcid.org/0000-0002-4106-2422 Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan Department of Mechatronics, AMM Lab Jeju National University, South KoreaSearch for more papers by this authorKapal Dev, Kapal Dev orcid.org/0000-0003-1262-8594 Department of Institute of Intelligent systems, University of Johannesburg, Johannesburg, South AfricaSearch for more papers by this authorRizwan A. Naqvi, Rizwan A. Naqvi orcid.org/0000-0002-7473-8441 Department of Unmanned Vehicle Engineering, Sejong University, Seoul, KoreaSearch for more papers by this authorYuanchun Zhou, Corresponding Author Yuanchun Zhou Zyc@cnic.cn orcid.org/0000-0003-2144-1131 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China Correspondence Yuanchun Zhou, Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, 100190 Beijing, China. Email: Zyc@cnic.cnSearch for more papers by this authorYi Du, Yi Du orcid.org/0000-0003-3121-8937 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this author Farah Deeba, Farah Deeba orcid.org/0000-0002-1987-2402 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorFayaz A. Dharejo, Fayaz A. Dharejo orcid.org/0000-0001-7685-3913 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorMuhammad Zawish, Muhammad Zawish orcid.org/0000-0002-4994-3443 Telecommunication Software and Systems Group, Waterford Institute of Technology, Waterford, IrelandSearch for more papers by this authorFida H. Memon, Fida H. Memon orcid.org/0000-0002-4106-2422 Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan Department of Mechatronics, AMM Lab Jeju National University, South KoreaSearch for more papers by this authorKapal Dev, Kapal Dev orcid.org/0000-0003-1262-8594 Department of Institute of Intelligent systems, University of Johannesburg, Johannesburg, South AfricaSearch for more papers by this authorRizwan A. Naqvi, Rizwan A. Naqvi orcid.org/0000-0002-7473-8441 Department of Unmanned Vehicle Engineering, Sejong University, Seoul, KoreaSearch for more papers by this authorYuanchun Zhou, Corresponding Author Yuanchun Zhou Zyc@cnic.cn orcid.org/0000-0003-2144-1131 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China Correspondence Yuanchun Zhou, Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, 100190 Beijing, China. Email: Zyc@cnic.cnSearch for more papers by this authorYi Du, Yi Du orcid.org/0000-0003-3121-8937 Department of Big Data, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this author First published: 24 August 2021 https://doi.org/10.1002/int.22627 Farah Deeba and Fayaz A. Dharejo are cofirst authors, hence contributed equally. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Apart from high-level computer vision tasks, deep learning has also made significant progress in low-level tasks, including single image dehazing. A well-detailed image looks realistic and natural with its clear edges and balanced colour. To achieve a clearer and vivid view, we exploit the role of edges and colours as a significant part of our proposed work. A progressive two-stage image dehazing network is presented to overcome the challenges of current image dehazing algorithms. The proposed image dehazing framework is divided into two steps; in the first stage, the multiscale image features of the encoder and decoder structure can be extracted. The second stage consists of the Color Correction Model (CCM), which retrieves balanced colour close to the ground truth. The encode-decoder network consists of a dense residual attention unit (DRAU) that comprises channel attention with pixel attention mechanisms. We have seen that weighted information and the haze difference is inconsistent across pixels without DRAU at the various channel-specific features. DRAU deals with different features and pixels unequally, which offers more versatility in handling knowledge of various types of detailed information. Our proposed two-stage network exceeds state-of-the-art algorithms in both visual and quantitative aspects. The findings are tested with the best-published peak signal-to-noise ratio metrics of 33.55–33.44 dB and SSIM 0.9619–0.9714 on SOTS indoor and outdoor test data sets. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation