13 November 2001 Spatially adaptive wavelet transform speckle noise-smoothing technique for SAR images
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In this work we propose a new wavelet transform based speckle denoising algorithm for SAR images. The algorithm will explicitly account for the signal dependent nature of the noise by studying the variances of detail wavelet coefficients. The algorithm will use the analysis of variance ANOVA technique to check if variances are due to means belonging to the same population or not. If neighboring variances indicate belonging to the same population, then it's a smooth region and coefficient should be smoothed. If neighboring variances indicate the presence of two different populations, then coefficient is due to image feature and should be preserved. This approach will provide the flexibility of adjusting to region intensity level and thus no need for the fixed threshold concept. The algorithm will take advantage of the fact that wavelet transform creates three detail sub-images and a coarse sub-image. Each detail sub-image is associated with frequency contents due to certain edge location and orientation. The algorithm will also consider using cross-information from all three-detail sub-images to decide whether coefficients are due to a feature and thus should be preserved, or they are due to noise and should be smoothed. Simulations will show that our algorithm will provide better performance in terms of PSNR, ENL , and visually than currently existing techniques.
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Yousef Hawwar, Yousef Hawwar, Ali Reza, Ali Reza, } "Spatially adaptive wavelet transform speckle noise-smoothing technique for SAR images", Proc. SPIE 4471, Algorithms and Systems for Optical Information Processing V, (13 November 2001); doi: 10.1117/12.449347; https://doi.org/10.1117/12.449347

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