A hybrid filtering method with no-reference quality assessment for synthetic aperture sonar images

Abstract

Synthetic Aperture Sonar (SAS) imaging technology is wildly used in the underwater applications. In the work process of SAS imaging, filtering technologies are important for SAS imaging, which can suppress different noises to improve signal quality. However, the existing filtering methods face many challenges, such as insufficient noise suppression, degradation of image detail, edge blurring and so on. Furthermore, the existing quality assessments for filtering methods are sometimes subjective, which limits the research development for filtering technologies. To solve these problems, we propose a hybrid filtering method with a no-reference quality assessment for SAS images in this paper. The proposed method includes two-stages, the first stage is to suppress local statistical interference, and the second stage is to preserve edge information by weighted smoothing. With the no-reference quality assessment, the hybrid filtering method and other filtering methods, including mid-value filtering and mean-value filtering methods, are investigated. The numerical results show that the no-reference quality assessment method can efficiently analyze different filtering methods, and the proposed methods can perform better than other filtering methods.