Speckle reduction of SAR images using adaptive curvelet domain

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 interpretation, analysis and classification of SAR images harder. In this paper we will consider the use of the curvelet transform (CT), for speckle reduction of SAR images. The CT is a new approach for image representation approach that codes image edges more efficiently then the wavelet transform. Edges are very important in image perception and with fewer coefficients to represent edges, a better denoising scheme can be achieved. We will use three denoising methods: Wavelet-domain hidden Markov tree models, hard thresholding of the curvelet coefficients, and an adaptive combined method (ACM) proposed here, which uses the desired aspects of both aforementioned methods.