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Mathematical Problems in Engineering
Volume 2017, Article ID 8190182, 10 pages
https://doi.org/10.1155/2017/8190182
Research Article

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

1School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China
2Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Sydney, NSW, Australia
3Nanjing College of Information Technology, Nanjing, China
4School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China

Correspondence should be addressed to Dengyin Zhang; nc.ude.tpujn@ydgnahz

Received 17 May 2017; Revised 11 September 2017; Accepted 12 October 2017; Published 7 November 2017

Academic Editor: Erik Cuevas

Copyright © 2017 Mingye Ju et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Single image haze removal has been a challenging task due to its super ill-posed nature. In this paper, we propose a novel single image algorithm that improves the detail and color of such degraded images. More concretely, we redefine a more reliable atmospheric scattering model (ASM) based on our previous work and the atmospheric point spread function (APSF). Further, by taking the haze density spatial feature into consideration, we design a scene-wise APSF kernel prediction mechanism to eliminate the multiple-scattering effect. With the redefined ASM and designed APSF, combined with the existing prior knowledge, the complex dehazing problem can be subtly converted into one-dimensional searching problem, which allows us to directly obtain the scene transmission and thereby recover visually realistic results via the proposed ASM. Experimental results verify that our algorithm outperforms several state-of-the-art dehazing techniques in terms of robustness, effectiveness, and efficiency.