Enhanced Wiener filter for ultrasound image restoration

Baselice, Fabio; Ferraioli, Giampaolo; Ambrosanio, Michele; Pascazio, Vito; Schirinzi, Gilda

doi:10.1016/j.cmpb.2017.10.006

Cited by 48 publications

(26 citation statements)

References 29 publications

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“…The latter is calculated in closed form according to [16] and [17], and it represents the ACF of the noise after the transformation of operator H 1 . Since its mean is different from zero, in order to apply the WF, the contribution of the mean has to be eliminated.…”

Section: Methodsmentioning

confidence: 99%

Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

et al. 2019

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Speckle noise is presented as an inherent dilemma that affects the image processing field, and in particular synthetic aperture radar images. In order to mitigate the adverse effects caused by this phenomenon, several approaches have been introduced in the scientific community during the last three decades including spatial-based and non-local filtering approaches. However, these proposed techniques suffer from some limitations. In fact, it is very difficult to find an approach that is able, on the one hand, to perform well in terms of noise reduction and image detail preservation and, on the other hand, provide a filtering output solution without high computational complexity and within a short processing time. In this paper, we aim to evaluate the performance of a newly-developed despeckling algorithm, presented as an enhancement of the classical Wiener filter and properly designed to work with a Graphics Processing Unit (GPU). The algorithm is tested on both a simulated framework and real Sentinel-1 SAR data. The results, obtained in comparison with other filters, are interesting and promising. Indeed, the proposed method turns out to be a useful filtering instrument in the case of large images by performing the processing within a limited time and ensuring good speckle noise reduction with a considerable image detail preservation.

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Section: Methodsmentioning

confidence: 99%

Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

et al. 2019

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“…, (16) where h k1,k2 (n, m) = cos π N n + 1 2 k 1 cos π M m + 1 2 k 2 . This 2-D function is the filter impulse response and the system with this impulse response generates the 2-D DCT-II coefficients which are sampled at (N − 1, M − 1).…”

Section: A 2-d Fir Filter Implementation For 2-d Dct-iimentioning

confidence: 99%

“…As mentioned earlier, the main drawback of ultrasound imaging is related to the low contrast resolution in ultrasound images due to the presence of speckle, which is a form of locally correlated multiplicative noise and generated by the interference of the acoustic energy from randomly distributed structure scatters. Several despeckling methods have been proposed in literature [15], [16]. Different filter families have been defined, each one with peculiar characteristics [17].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

et al. 2020

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In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images.

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“…14 WF is a widely accepted image restoration method which is based on minimizing the mean square error between the ideal image estimate and the observed image. 12,13,[16][17][18] In addition, WF can be rapidly implemented in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Single-image Bayesian Restoration and Multi-image Super-resolution Restoration for B-mode Ultrasound Using an Accurate System Model Involving Correlated Nature of the Speckle Noise

2019

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B-mode ultrasound is an essential part of radiological examinations due to its low cost, safety, and portability, but has the drawbacks of the speckle noise and output of most systems is two-dimensional (2D) cross sections. Image restoration techniques, using mathematical models for image degradation and noise, can be used to boost resolution (deconvolution) as well as to reduce the speckle. In this study, new single-image Bayesian restoration (BR) and multi-image super-resolution restoration (BSRR) methods are proposed for in-plane B-mode ultrasound images. The spatially correlated nature of the speckle was modeled, allowing for examination of two different models for BR and BSRR for uncorrelated Gaussian (BR-UG, BSRR-UG) and correlated Gaussian (BR-CG, BSRR-CG). The performances of these models were compared with common image restoration methods (Wiener filter, bilateral filtering, and anisotropic diffusion). Well-recognized metrics (peak signal-to-noise ratio, contrast-to-noise ratio, and normalized information density) were used for algorithm free-parameter estimation and objective evaluations. The methods were tested using superficial tissue (2D scan data collected from volunteers, tissue-mimicking resolutions, and breast phantoms). Improvement in image quality was assessed by experts using visual grading analysis. In general, BSRR-CG performed better than all other methods. A potential downside of BSRR-CG is increased computation time, which can be addressed by the use of high-performance graphics processing units (GPUs).

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Enhanced Wiener filter for ultrasound image restoration

Cited by 48 publications

References 29 publications

Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

Single-image Bayesian Restoration and Multi-image Super-resolution Restoration for B-mode Ultrasound Using an Accurate System Model Involving Correlated Nature of the Speckle Noise

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