Open Access

ARTICLE

Multiscale Image Dehazing and Restoration: An Application for Visual Surveillance

by Samia Riaz¹, Muhammad Waqas Anwar², Irfan Riaz³, Hyun-Woo Kim⁴, Yunyoung Nam^4,*, Muhammad Attique Khan⁵

1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, 47040, Pakistan
2 Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
3 Department of Electronics and Communication Engineering, Hanyang University, Ansan, Korea
4 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea
5 Department of Computer Science, HITEC University Taxila, Taxila, 47080, Pakistan

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2022, 70(1), 1-17. https://doi.org/10.32604/cmc.2022.018268

Received 02 March 2021; Accepted 03 April 2021; Issue published 07 September 2021

Abstract

The captured outdoor images and videos may appear blurred due to haze, fog, and bad weather conditions. Water droplets or dust particles in the atmosphere cause the light to scatter, resulting in very limited scene discernibility and deterioration in the quality of the image captured. Currently, image dehazing has gained much popularity because of its usability in a wide variety of applications. Various algorithms have been proposed to solve this ill-posed problem. These algorithms provide quite promising results in some cases, but they include undesirable artifacts and noise in haze patches in adverse cases. Some of these techniques take unrealistic processing time for high image resolution. In this paper, to achieve real-time halo-free dehazing, fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches. It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images. A coarse transmission map is estimated by using the minimum of different size patches. Then a cascaded fast guided filter is used to refine the transmission map. We introduce an efficient scaling technique for transmission map estimation, which gives an advantage of very low-performance degradation for a high-resolution image. For performance evaluation, quantitative, qualitative and computational time comparisons have been performed, which provide quiet faithful results in speed, quality, and reliability of handling bright surfaces.

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Citations