Inferring the level of visibility from hazy images
Alexander Agung Santoso Gunawan, Heri Prasetyo, Indah Werdiningsih, Janson Hendryli · International Journal of Business Intelligence and Data Mining · 2020
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.