SAR speckle reduction using wavelet denoising and Markov random field modeling

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

435 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/68be5362d6ea714d1e2e7623aed0927608382ebb

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

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Citations in the corpus

SAR Image Despeckling Using a Convolutional Neural Network (2017, methodology): “Various methods have been developed in the literature to suppress speckle including multilook processing [3], [4], filtering methods [5]–[7], wavelet-based despecking methods [8]–[11], block-matching 3D (BM3D) algorithm [12] and total variation (TV) methods [13].”
Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “either spatially, where local spatial filters, such as the Lee filter [123], Kuan filter [124], and Frost filter [125], are employed, or using wavelet-based methods [126], [127], [128].”
SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling (2003, methodology): “Therefore, as proposed by Xie et al. [17], this biased mean should be corrected by adding an additional step " adjust mean " between the " IDWT " and the " EXP " modules of our processor (cf. Fig.”
A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “In [71], MMSE estimation have been used after modeling wavelet coefficients by means of Gaussian mixtures and Markov random fields to characterize their spatial and interscale dependency.”
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. (2013, background): “This article reviews some of the most recent advances in breast cancer detection/diagnosis using CAD systems developed for mammography and ultrasound.”
NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising (2015, background): “achieves the best tradeoff between data fidelity and regularity, as defined in terms of wavelets coefficients distribution [7]–[12] or total variation of the image [13]–[16].”
Automated breast cancer detection and classification using ultrasound images: A survey (2015, methodology): “The drawback of thresholding methods is that the choice of the threshold is usually done in an ad hoc manner.”
Improved Sigma Filter for Speckle Filtering of SAR Imagery (2009, methodology): “effective in speckle filtering, which is comparable to recently developed algorithms, such as those based on wavelet transforms [9], [11].”