Comparing Undecimated Wavelet, Nonsubsampled Contourlet and Shearlet for Sar Images Despeckling

Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
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JR_MJEE-9-3_002

تاریخ نمایه سازی: 12 شهریور 1402

Abstract:

Synthetic Aperture Radar (SAR) images suffer of multiplicative speckle noise, which damages the radiometric resolution of SAR images and makes the data interpretation difficult. Bayesian shrinkage in a transformed domain is a well-known method based on finding threshold value to suppress the speckle noise. This paper present a new approach to obtain the optimum threshold values for Bayesian shrinkage. For this purpose, we use undecimated wavelet transform (UWT), nonsubsampled Contourlet transform (NSCT), and nonsubsampled Shearlet transform (NSST).According to our experimental results, transformed coefficients influenced by noise differently. It means that some coefficients in transformed domain belong to the specific subband are more robust against noise. We use this new found property in order to determine the optimum threshold value and developed our proposed method named weighted Bayesian Shrinkage in transformed domain. Our experimental results show that finding the optimum threshold value in Shearlet domain outperforms both Contourlet and undecimated wavelet transform.Although the weighted Bayesian Shrinkage in NSCT used before, the weighted Bayesian Shrinkage in NSST is applied in this paper for the first time.

Authors

Sedigheh Ghofrani

Electrical and Electronic Engineering Department, Islamic Azad University, Tehran South Branch

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