Statistical properties of logarithmically transformed speckle

By Hua Xie, Leland E. Pierce, Fawwaz T. Ulaby. IEEE Transactions on Geoscience and Remote Sensing, 2002

343 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Oceanography

View Paper: https://www.semanticscholar.org/paper/fdb0de97f1a70ad420bbcc7ab9237a6872930541

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Oceanography

Citations in the corpus

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising (2015, background): “A bias-correction step is necessary to correct for non-Gaussianity in log-space [34].”
A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “other measures derived from the MSE, like the signal–to–noise ratio (SNR), the peak signal–to–noise ratio (PSNR) and the signal–to–mean square error (SNRS), have been widely used for the quality assessment of both denoising and despeckling [33], [57], [76].”
A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage (2012, methodology): “SAR-like images are obtained by multiplying optical images by simulated white speckle in amplitude format (square-root intensity model) [42] with pdfs corresponding to the cases of 1, 2, 4, and 16 looks.”
SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling (2003, methodology): “We should note here that due to the use of the logarithmic transformation, the mean of the log-transformed speckle field is biased [32].”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “All these methods require considerable A u t h o r ' s p e r s o n a l”
Speckle removal from SAR images in the undecimated wavelet domain (2002, background): “The major inconvenience of such an approach is that the backscatter mean is not preserved in homogeneous areas when the logarithm is inverted after filtering [9], [ 10 ].”
SAR speckle reduction using wavelet denoising and Markov random field modeling (2002, methodology): “As shown in [22], the mean of the log-transformed speckle noise is not zero, whereas most wavelet-based denoising techniques assume AWGN with zero mean.”