Abstract:

A shearlet domain synthetic aperture radar(SAR) image denoising algorithm based on Bayesian model is presented, through the characteristic analysis of the SAR image noise. Firstly, the SAR image in the shearlet domain is represented sparsely to obtain the distribution of the sparse coefficient. Secondly, the signal and noise detection modeling is carried out by using the Bayesian model to solve the problem of the optimal threshold. Then, the SAR image noise is smoothed by using the adaptive weighting algorithm, according to different characteristics of the correlation of the sparse coefficient in different directions. Finally, conducting the inverse shearlet transform by using the high and the low frequency sub images of the noise reduction to obtain the SAR reconstruction image. The experimental results show that the proposed algorithm can suppress speckle, as well as can restrain the image edge information better by means of the experiment in MSTAR database.