Maritime filtering for images and videos

Maritime signal processing technologies have emerged as an important area of study because of the increasing popularity of autonomous ships and automatic maritime surveillance systems. However, the various techniques developed for detecting or tracking objects remain unable to address various maritime noise challenges that cause several types of false positives in maritime visual surveillance. Maritime signal processing is challenging because of the prevalence of noise sources such as severe dynamic backgrounds, wakes, and reflections, owing to the complex, unconstrained, and diverse nature of such scenes caused by the surface properties of water. Moreover, few studies have investigated specific maritime noise filtering as a general integrated processing approach with image and video technologies in the context of maritime visual surveillance. In this study, we propose a novel maritime noise prior (MNP) based on a dark channel prior and observations of the characteristics of the sea. A general maritime filtering technique is developed to suppress noise originating from the properties of water in maritime images and videos. The proposed method employs a noniterative, nonlinear, and simple maritime filtering approach based on MNP that does not require specialized knowledge of application scene conditions or structure. We conducted image and video experiments by applying our approach to three publicly available databases. In experiments with color images, our method successfully filtered related background noise and water, i.e., severe boat wakes and reflections, while preserving objects other than water in color images. In the experiments with video sequences, the results demonstrated that the proposed filter improved the overall performance of state-of-the-art background subtraction (BS) algorithms from 36.60%-50.63%. By combining BS algorithms and filtering to enhance foreground detection in video sequences, the proposed method ensures the universal applicability and flexibility required to eliminate noise from images and videos obtained in challenging maritime environments. The results indicate that the proposed method is appropriate for maritime surveillance applications implementing image segmentation and foreground detection, and it can potentially increase the accuracy of maritime visual surveillance.