A new wavelet thresholding approach for SAS images denoising

SAS (Synthetic Aperture Sonar) is actively used in sea bed imagery. Indeed high resolution images provided by SAS are of great interest, especially for the detection, localization and eventually classification of objects lying on sea bed. Unfortunately, SAS images are highly corrupted by a granular multiplicative noise, called speckle noise, which reduces spatial and radiometric resolutions. The purpose of this article is to present a new wavelet thresholding approach, which ensures both a strong denoising and a spatial resolution preservation. Results obtained on real SAS data are presented and compared with those obtained using classing wavelet-based denoising approaches.