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Simultaneous Speckle Reduction and SAR Image Compression Using Multiwavelet Transform

Synthetic aperture radar (SAR) images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees (SPIHT) algorithm, our method achieves favorable peak signal to noise ratio (PSNR) and superior speckle noise reduction performances. can be efficiently combined together in a single process of decomposition and reconstruction in wavelet transform. Recent developed multiwavelets can possess compact support, orthogonality, symmetry and high order vanish moments simultaneously which are desirable properties for best performance in image compression while scalar wavelets cannot. Thus multiwavelets offer the possibility of superior performance in obviating some of the limitations of scalar wavelets. At the present time, research on the application of multiwavelets in SAR image compression is relatively few. In this work, we explore a SAR image compression algorithm based on multiwavelets and soft-thresholding, which firstly conducts speckle noise reduction by modified soft-thresholding, and then compresses the image using multiwavelets-based set partitioning in hierarchical trees (SPIHT) algorithm (1) .