Combined wavelet and curvelet denoising of SAR images

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. Logarithmic transformation of SAR images convert the multiplicative noise models to additive noise. In this paper, two combinations of time invariant wavelet and curvelet transforms will be used for denoising of SAR images. The first one is called the combined filtering algorithm (CFA). This method is based on a constrained optimization problem, both in the wavelet and curvelet domains. The second method is called the adaptive combined method (ACM) which uses the wavelet transform to denoise homogeneous areas and the curvelet transform to denoise areas with edges