A blind contour‐aware quality model for sonar images

Abstract

Abstract Since the penetration depth of visible light is limited in muddy and dark deep marine environment, sonar imaging, which is independent of natural light, has been attracting much attention in underwater detections. However, the visual quality of sonar image is inevitably damaged during the acquisition and the transmission in the sophisticated and dynamic underwater environment. The quality decrease can further bring negative impacts on oceanic information analysis. To measure the quality decrease of sonar images, this paper proposes a contour‐aware model for sonar image quality assessment (CaMSIQA). The CaMSIQA metric is established upon the conclusion that the quality of sonar images can be defined as a measure of their utilities in real application scenarios. We exploit a contour information statistic (CIS) model that is critical to object recognition of sonar images here. Quantifying the changes of the CIS model between the unimpaired and test sonar images makes it possible to perceive the quality decrease of sonar images. Extensive experimental results have demonstrated the effectiveness of the proposed method compared with current mainstream image quality assessment (IQA) methods without full reference information.