SAR image despeckling using adaptive multiscale products thresholding in directionlet domain

Abstract

A directionlet transform (DT)-based multiscale products thresholding is presented which uses a generalised cross-validation (GCV) technique for synthetic aperture radar image despeckling. DT has gained popularity over the past few years as an anisotropic, critically sampled and perfect reconstruction transform with directional vanishing moments along any two directions. In this reported work, the multiscale correlation of DT is exploited by multiplying the adjacent scale coefficients to enhance edge structures while weakening noise. An optimal subband adaptive threshold based on GCV is then computed using this multiscale product. The despeckled image is finally synthesised by using the threshold applied DT coefficients. The employment of DT results in the significant features in images evolving with high magnitudes across scales, while the noise decays rapidly. The proposed scheme outperforms many of the traditional despeckling schemes in terms of speckle reduction and edge preservation.