Title: Inland river image dehazing algorithm based on water surface depth prior
Authors: ZhongYi Hu; ChangZu Chen; Qi Wu; MianLu Zou; YuLian Cao; MingHai Xu; ZuoYong Li
Addresses: Intelligent Information Systems Institute, Department of Computer and Artificial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, China ' Intelligent Information Systems Institute, Department of Computer and Artificial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, China ' Intelligent Information Systems Institute, Department of Computer and Artificial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, China ' Intelligent Information Systems Institute, Department of Computer and Artificial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, China ' Faculty of Science, School of Aviation, University New South Wales, NSW 2052, Sydney, Australia ' Intelligent Information Systems Institute, Department of Computer and Artificial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, China ' Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350121, Fujian, China
Abstract: This study proposes a single-image restoration algorithm requiring no additional scene information that is suitable for the removal of haze from images of inland waterways. This algorithm uses a water surface depth of focus prior to obtaining a rough atmospheric-light transmission image, and then applies guided filter refinement and sky segmentation based on grey level histograms to estimate the atmospheric light intensity, thereby performing image dehazing automatically. The performance and applicability of our proposed algorithm are verified by the dehazing results obtained using the proposed algorithm for a large sample set of hazy images of inland waterways compared with those obtained using two standard single-image dehazing algorithms in terms of the processed image quality and processing speed. The results confirm the reliability of the water surface depth of focus prior model. Our method is appropriate for inland waterway images and provides better image quality and computational performance than the existing algorithms.
Keywords: guided filter; image dehazing; inland river image; water surface depth prior.
DOI: 10.1504/IJCAT.2020.107919
International Journal of Computer Applications in Technology, 2020 Vol.63 No.1/2, pp.160 - 172
Received: 06 Dec 2019
Accepted: 30 Jan 2020
Published online: 30 Jun 2020 *