The Contourlet-based Statistical Models For SAR Images Denoising

The presence of speckle noise in SAR images is undesirable, and it makes scene analysis and image understanding very difficult. Thus, speckle reduction is very important for many SAR images processing tasks, for example edge detection and image segmentation.The traditional denoising methods including airspace domain and transform domain methods don't work effectively when using for SAR images denoising. When using these methods, some details are lost and the noises of the homogeneous regions can not be effectively disposed of. However, the appearance of statistical models in multiscale geometric transform domain provides kinds of newly developed models for SAR images denoising.This paper mainly studies Multiscale Geometric Analysis (MGA) tools, i.e. Contourlet and Contourlet based statistical models, on the application of SAR images denoising. Three new methods are proposed in this paper. The main innovative points are as follows:(1) Contourlet-based Hidden Markov Tree (HMT) Model and Contextual Hidden Markov Model (CHMM) for SAR Images Denoising, this method bases on the fact that Contourlet coefficients bear strong correlations between neighbors and interscales. The CHMM is combined with the HMT to establish statistical models in Contourlet domain. Cycle Spinning will be employed to avoid the artifacts which are caused by translation invariance of the Contourlet transform. Simultaneous, the anisotropic diffusion is used to reinforce the detail information.(2) Contourlet-based Block Hidden Markov Model (BHMM) for SAR Images Denoising, the BHMM is constructed to characterize the neighbors'dependence of Contourlet coefficients, which is used for SAR images despeckling.(3) Nonsubsampled Contourlet Transform (NSCT) based Edge Detection and Prior Spatial Constraints for SAR Images Denoising, first using edge detector to label the NSCT coefficients as three classes, then using BHMM for SAR image denoising, at last using prior spatial constraints to deal with difference image, then addition is performed for the two denoising images to get the final denoising result.The experimental results using real SAR images show that the proposed method outperforms the classical spatial filters and despeckling methods based on others transformed domain.