Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the
coherent nature of the scattering phenomenon. This paper proposed an adaptive regularized approach to reduce SAR
image speckle based on least squares support vector machines (LS-SVM). Generally, SAR images are comprised of
multiple features of different spatial scales, and there is typically a trade-off between speckle removal and detail
preservation. A natural approach to partially alleviate this problem is to use spatial adaptive regularization parameter on
the use of regularized procedure. Here, each pixel has its own associated regularization parameter in this paper, instead
of choosing a global regularization parameter. Experimental results show that our approach has a good performance on
the speckle reduction without destruction of important SAR image details.
Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a
transform domain. Building on previous work, a novel denoising method based on local adaptive multi-scale wavelet
least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has
demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of
the edges and details.