A variational approach for adaptive underwater sonar image denoising
Deshan Chen, Xiumin Chu, Feng Ma, Xuanxuan Teng · 2017
Underwater sonar imaging is a significant means for a variety of maritime work. However, the sonar images are corrupted by signal dependent speckle noise, which restricts the potential practical applications. In this paper, we propose a novel variational approach that addresses the adaptive sonar image denoising problem. To accurately describe the signal dependent characteristic of noise for real sensors, we utilize a generalized noise model, which can be adapted to represent various types of noise. With the generalized noise model, we formulate the denoising problem via a variational approach. Finally, we present a modified primal and dual method to efficiently solve the variational minimization problem. Experimental results on both simulated and real data validate the effectiveness and efficiency of our method.