Title: Inferring the level of visibility from hazy images

Authors: Alexander A.S. Gunawan; Heri Prasetyo; Indah Werdiningsih; Janson Hendryli

Addresses: Mathematics Department, Bina Nusantara University, KH Syahdan 9, Jakarta 11530, Indonesia ' Informatics Department, Sebelas Maret University, Ir. Sutami No. 36A, Surakarta, Jawa Tengah, Indonesia ' Mathematics Department, Airlangga University, Muljorejo Kampus C, Surabaya, Indonesia ' Informatics Engineering Department, Tarumanagara University, Letjen S. Parman No. 1, Jakarta, Indonesia

Abstract: In our research, we would like to exploit crowdsourced photos from social media to create low-cost fire disaster sensors. The main problem is to analyse how hazy the environment looks like. Therefore, we provide a brief survey of methods dealing with visibility level of hazy images. The methods are divided into two categories: single-image approach and learning-based approach. The survey begins with discussing single image approach. This approach is represented by visibility metric based on contrast-to-noise ratio (CNR) and similarity index between hazy image and its dehazing image. This is followed by a survey of learning-based approach using two contrast approaches that is: 1) based on theoretical foundation of transmission light, combining with the depth image using new deep learning method; 2) based on black-box method by employing convolutional neural networks (CNN) on hazy images.

Keywords: hazy image; visibility level; single image approach; learning-based approach; social media.

DOI: 10.1504/IJBIDM.2020.104739

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.2, pp.177 - 189

Received: 28 Feb 2017
Accepted: 22 Aug 2017

Published online: 20 Jan 2020 *

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