Anisotropic Total Variation Regularized Low-Rank Approximation for SSS Images Radiometric Distortion Correction

Radiometric distortion caused by the time-varying gain (TVG), beam patterns, angular responses, and sonar altitude variations, highly degrades the quality of side-scan sonar (SSS) images. Thus, radiometric distortion correction becomes a fundamental step for SSS image processing, which holds vital importance for geomorphic applications. However, existing methods cannot take the prior information of the acoustic illumination component as well as the feature of seafloor into consideration well, which would easily cause damage to the image and also always be powerless for residual stripe noise. In this article, a novel radiometric correction method is proposed. First, we give a detailed analysis of the SSS imaging theory based on Lambert's law as well as prior knowledge about the characteristics of SSS images. Then, incorporating the prior of the SSS imaging process, the low-rank constraint is specifically introduced for the illumination component, while the anisotropic total variation (ATV) constraint is used to constraint the albedo component; combining other constraints, a decomposition model is proposed to correct the radiometric distortion based on the SSS imaging theory. Also, an alternative minimization method has been adopted to solve the proposed model effectively. Experiments proved the validity of the proposed method.

Automated summary

In this article, a novel radiometric correction method is proposed for side-scan sonar (SSS) images. By analyzing the SSS imaging theory and prior knowledge about SSS image characteristics, a decomposition model based on the SSS imaging theory is proposed, using low-rank constraint and anisotropic total variation constraint to constrain the illumination and albedo components of the image for radiometric distortion correction.