Inferring the level of visibility from hazy images Online publication date: Thu, 30-Jan-2020
by Alexander A.S. Gunawan; Heri Prasetyo; Indah Werdiningsih; Janson Hendryli
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 16, No. 2, 2020
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.
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