Segmentation-Based MAP Despeckling of SAR Images in the Undecimated Wavelet Domain

Bianchi, Tiziano; Argenti, Fabrizio; Alparone, Luciano

doi:10.1109/tgrs.2008.920018

Cited by 107 publications

(53 citation statements)

References 39 publications

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“…Note that the NL-means filter corresponds here to the non-iterative PPB filter. For multiplicative GSN, the comparisons have been performed with the Wavelet-based Image-denoising Nonlinear SAR (WIN-SAR) filter [11] and the MAP filter based on Undecimated Wavelet Decomposition and image Segmentation (MAP-UWD-S) [13]. Figures 3 and 4 present the obtained denoised images for the images corrupted respectively by additive WGN with a standard deviation σ = 40 and by multiplicative GSN with an equivalent number of look L = 3.…”

Section: A Results On Synthetic Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights

Deledalle

Denis

Tupin

2009

IEEE Trans. on Image Process.

700

514

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Abstract-Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the Non Local means (NL means) algorithm introduced by Buades et al.[1], which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a datadriven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as Synthetic Aperture Radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case.

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Section: A Results On Synthetic Imagesmentioning

confidence: 99%

“…Noise can then be strongly suppressed by zeroing the least significant coefficients. Such approaches can be applied to additive Gaussian noise [6], [7] and have been extended to multiplicative speckle noise [8]- [13]. They can be improved by using shape-adaptive domains [14], and sparse decompositions with over-complete or learned dictionaries [15]- [17].…”

mentioning

confidence: 99%

Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights

Deledalle

Denis

Tupin

2009

IEEE Trans. on Image Process.

700

514

View full text Add to dashboard Cite

show abstract

“…Therefore, some form of despeckling is routinely used by both photo-interpreters and computer programs before proceeding with specific tasks. Research on this topic has been very intense, and many techniques have been proposed based, for example, on adaptive linear filtering [1], or wavelet shrinkage [2] and, recently, on nonlocal filtering [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Fast Adaptive Nonlocal SAR Despeckling

Cozzolino

Parrilli

Scarpa

et al. 2014

IEEE Geosci. Remote Sensing Lett.

162

101

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Despeckling techniques based on the nonlocal approach provide an excellent performance, but exhibit also a remarkable complexity, unsuited to time-critical applications. In this letter, we propose a fast nonlocal despeckling filter. Starting from the recent SAR-BM3D algorithm, we propose to use a variable-size search area driven by the activity level of each patch, and a probabilistic early termination approach that exploits speckle statistics in order to speed up block matching. Finally, the use of look-up tables helps in further reducing the processing costs. The technique proposed conjugates excellent performance and low complexity, as demonstrated on both simulated and real-world SAR images and on a dedicated SAR despeckling benchmark.

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“…Most of the wavelet-based techniques [12][13][14][15][16][17][18][19] resort to the statistical wavelet shrinkage combined with MAP criterion. In general, the wavelet-based algorithms guarantee an overall performance enhancement compared with the spatialdomain techniques, and a superior ability to preserve signal resolution.…”

Section: Introductionmentioning

confidence: 99%

Synthetic aperture radar image despeckling via total generalised variation approach

Feng

Liu

Qiao

2015

IET Image Processing

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Speckle reduction is an important task in synthetic aperture radar. One extensively used approach is based on total variation (TV) regularisation, which can realise significantly sharp edges, but on the other hand brings in the undesirable staircasing artefacts. In essence, the TV-based methods tend to create piecewise-constant images even in regions with smooth transitions. In this study, a new method is proposed for speckle reduction via total generalised variation (TGV) penalty. This is reasonable from the fact that the TGV-based model can reduce the staircasing artefacts of TV by being aware of higherorder smoothness. An efficient numerical scheme based on the Nesterov's algorithm is also developed for solving the TGVbased optimisation problem. Monte Carlo experiments show that the proposed scheme yields state-of-the-art results in terms of both performance and speed. Especially when the image has some higher-order smoothness, the authors' scheme outperforms the TV-based methods.

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Segmentation-Based MAP Despeckling of SAR Images in the Undecimated Wavelet Domain

Cited by 107 publications

References 39 publications

Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights

Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights

Fast Adaptive Nonlocal SAR Despeckling

Synthetic aperture radar image despeckling via total generalised variation approach

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