SAR Image Denoising Based on Orthogonal Bandelet with Context-Model and GCV-Threshold
Jingwen Yan, Daxiang Huang, Anfa Zhang, Gang Lü · 2009
A novel approach to synthetic aperture radar (SAR) image denoising is presented in this paper, which is based on the second generation bandelet with contextual model and multi-level generalized cross validation (GCV) threshold. Orthogonal bandelet, a multiresolution geometry analysis tool, uses an adaptive segmentation and a local geometric flow suited to capture the anisotropic regularity of edge structures and then provide an optimal representation of noisy SAR image. The contextual model is used to compute contextual values of bandelet coefficients and GCV rule is used to get optimal threshold for contextual values on each scale. Experimental results using real SAR image demonstrate that the method can remove the speckle noise efficiently and preserve edge of image better.