A Bayesian Joint Decorrelation and Despeckling of SAR Imagery

Wang, Caifeng; Xu, Linlin; Clausi, David A.; Wong, Alexander

doi:10.1109/lgrs.2019.2899773

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…As our proposed framework preserves original image features such as texture, details and edges, it may have a far reaching impact on medical imaging for diagnostic purpose. At the same time, the proposed despeckling algorithm may be efficacious in dealing with the speckle noise problem in other imaging such as synthetic aperture radar (SAR) [41] and optical coherence tomography (OCT) [42].…”

Section: Discussionmentioning

confidence: 99%

Multiframe-based Adaptive Despeckling Algorithm for Ultrasound B-mode Imaging with Superior Edge and Texture

Dey,

Hasan

2019

Preprint

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Removing speckle noise from medical ultrasound images while preserving image features without introducing artifact and distortion is a major challenge in ultrasound image restoration. In this paper, we propose a multiframe-based adaptive despeckling (MADS) algorithm to reconstruct a highresolution B-mode image from raw radio-frequency (RF) data that is based on a multiple input single output (MISO) model. As a prior step to despeckling, the speckle pattern in each frame is estimated using a novel multiframe-based adaptive approach for ultrasonic speckle noise estimation (MSNE) based on a single input multiple output (SIMO) modeling of consecutive deconvolved ultrasound image frames. The elegance of the proposed despeckling algorithm is that it addresses the despeckling problem by completely following the signal generation model unlike conventional ad-hoc smoothening or filtering based approaches, and therefore, it is likely to maximally preserve the image features. As deconvolution is a necessary pre-processing step to despeckling, we describe here a 2-D extension of the SIMO model-based 1-D deconvolution method. Finally, a complete framework for the generation of high-resolution ultrasound B-mode image has been also established in this paper. The results show 8.55 − 15.91 dB, 8.24 − 14.94 dB improvement in terms of SNR and PSNR, respectively, for simulation data and 2.22 − 3.17, 13.24 − 32.85 improvement in terms of NIQE and BRISQUE, respectively, for in-vivo data compared to the traditional despeckling algorithms. Visual comparison shows superior texture, resolution, details of B-mode images offered by our method compared to those by a commercial scanner, and hence, it may significantly improve the diagnostic quality of ultrasound images.

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Section: Discussionmentioning

confidence: 99%

Multiframe-based Adaptive Despeckling Algorithm for Ultrasound B-mode Imaging with Superior Edge and Texture

Dey,

Hasan

2019

Preprint

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“…In the analysis of SAR imaging, three apparent causes of this granular distortion can be pointed out: propagation, antenna pattern, and the imaging process, which all result in speckle [29]. As for the effect of propagation, interference of the de-phased but coherent scattered waves after reflection and propagation results in the granular spots called speckles.…”

Section: Principle Of Speckle Noisementioning

confidence: 99%

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Wang

et al. 2022

Remote Sensing

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Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising models is proposed in this paper. Taking full account of the local statistics in the image, a quantization technique—which is different from the normal edge detection method—is supported by the variation coefficient of the image. Accordingly, a quantizer is designed to respond to both noise level and edge strength. This quantizer automatically determines the threshold of diffusion coefficient and controls the weight between total variation filter and anisotropic diffusion partial differential equation filter. A series of experiments are conducted to test the performance of the quantizer and proposed filter. Extensive experimental results have demonstrated the superiority of the proposed method with both synthetic images and natural SAR images.

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“…Hence, (2) is equivalent to the following minimisation problem:

f_{MAP} = \arg \min_{f} falsefalse{l false(bold-italicg; bold-italicf false) + λ η false(bold-italicf false) falsefalse},

where

l false(bold-italicg; bold-italicf false) = - \log false(p (bold-italicg | bold-italicf) false)

corresponds to the data fidelity term and

λ η false(bold-italicf false) = - \log false(p false(bold-italicf false) false)

is the regulariser. The likelihood function for Rayleigh distributed speckle is given by [18]

p false(g | f false) = \prod_{i = 1}^{N} \frac{g_{i}}{f_{i}} \exp (- \frac{g_{i}^{2}}{2 f_{i}}) .

It is straight forward that the data fidelity term is (see [26] for more details)

l false(bold-italicg; bold-italicf false) = \sum_{i = 1}^{N} (}{, \log false(f_{i} false) + \frac{g_{i}^{2}}{2 f_{i}}) .

In [22], it was stated that from speckle interferometry, the noise in SAR imagery can be modelled as Poisson distribution. The likelihood function for Poisson distributed speckle is given by [27]

p false(g | f false) = \prod_{i = 1}^{N} e^{- f_{i} <...>}

…”

Section: Problem Formulationmentioning

confidence: 99%

“…Afonso and Sanches [21] employed a blind inpainting methodology for non‐Gaussian noisy models such as Rayleigh and Poisson using TV and

ℓ_{0}

regularisers and solved it using ADMM for medical images. Wang et al [22] came up with the Bayesian joint decorrelation and despeckling method for the restoring of SAR images contaminated by Poisson speckle. Seminal contributions have been made by Venkatakrishnan et al [5] regarding PnP ADMM, which can be considered as a modification of ADMM, where the image denoising is plugged as an intermediate step.…”

Section: Introductionmentioning

confidence: 99%