Combined wavelet and curvelet denoising of SAR images using TV segmentation

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the images and makes interpretations, analysis and classifications of SAR images harder. Therefore, some speckle reduction is necessary prior to the processing of SAR images. The speckle noise can be modeled as multiplicative i.i.d. Rayleigh noise. The discrete curvelet transform is a new image representation approach that codes image edges more efficiently than the wavelet transform. On the other hand, wavelet transform codes homogeneous areas better than curvelet transform. In this paper, two combinations of time invariant wavelet and curvelet transforms will be used for denoising of SAR images. Both of the methods use the wavelet transform to denoise homogeneous areas and the curvelet transform to denoise areas with edges. The segmentation between homogeneous areas and areas with edges is done by using total variation segmentation. Simulation results suggested that these denoised schemas can achieve good and clean images.