Speckle removal from SAR images in the undecimated wavelet domain
By Fabrizio Argenti, Luciano Alparone. IEEE Transactions on Geoscience and Remote Sensing, 2002
360 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/7901e5a12f540c51b45a0b3f536407568d9b8da6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Vishal M. Patel
- He Zhang
- Luisa Verdoliva
- Francesca Bovolo
- Lorenzo Bruzzone
- Puyang Wang
- Xiaoxiang Zhu
- Lichao Mou
In Media Technology
- Lorenzo Bruzzone
- Francesca Bovolo
- Lichao Mou
- Xiaoxiang Zhu
- Vishal M. Patel
- He Zhang
- Puyang Wang
- Luisa Verdoliva
Citations in the corpus
- A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “to (37), following the same approach as in [108]; hardthresholding and Wiener filtering are replaced with a local linear minimum mean square error (LLMMSE) estimator [78] based on the additive signal-dependent noise model in (25).”
- A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage (2012, methodology): “As for the noise, this problem was addressed in [20] with reference to the UDWT case, obtaining E V 2 (i) = σ 2 u (1 + σ 2 u ) k h 2 (k)E z 2 (i − k) (18) where h is the subband equivalent filter, σ 2 u is a known parameter depending on speckle format and number of looks [42], and k spans a 7 × 7…”
- 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].”
- 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 Despeckling Using a Convolutional Neural Network (2017, methodology): “Various methods have been developed in the literature to suppress speckle including multi-look processing [4], [5], filtering methods [6], [7], [8], wavelet-based despecking methods [9], [10], [11], [12], block-matching 3D (BM3D) algorithm [13] and Total Variation (TV) methods [14].”
- A detail-preserving scale-driven approach to change detection in multitemporal SAR images (2005, methodology): “To define whether a pixel belongs to a border or a homogeneous area at a given scale , we propose to use a multiscale local coefficient of variation LCV , as typically done in adaptive speckle denoising algorithms [ 28 ], [32].”