Robust image denoising for sonar imagery

The recent boost in undersea operations has led to the development of high-resolution sonar systems mounted on autonomous vehicles, and aimed to scan the sea floor and detect objects. An important part of sonar detection is the image denoising, where the background is smoothed and noise components are removed while preserving the object’s borders. Sonar image denoising is a challenging task, mostly due to the heavy intensity inhomogeneity of the background and the heavy spatial varying background. In this paper, we propose an algorithm for sonar image denoising that is based on the adaptation of the nonlocal means-based filter. The noise in the highlight and background regions is modeled by the exponential distribution, while the noise in the shadow region is modeled by the Gaussian distribution. We estimate the label of each pixel through image segmentation to estimate the parameters of each distribution. Then, the minimum entropy criteria is used to decide which statistics model in the denoising filter gives the best results. Results for synthetic sonar images and over real sonar images demonstrate that the proposed method successfully removes the noise components while preserving the object’s edges.