Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

Authors

  • Zengguo Sun Key Laboratory of Modern Teaching Technology, Ministry of Education
  • Rui Shi Shaansxi Normal University
  • Wei Wei Xi'an University of Technology

DOI:

https://doi.org/10.5755/j01.itc.49.3.23998

Keywords:

SAR images; despeckling algorithm, non-subsampled Shearlet transform, non-local means

Abstract

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

Downloads

Published

2020-09-28

Issue

Section

Articles