Robust Image Denoising for Sonar Imagery

Abstract—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.