A New Non-Linear Hyperbolic-Parabolic Coupled PDE Model for Image Despeckling

Majee, Sudeb; Ray, Rajendra K.; Majee, Ananta K.

doi:10.1109/tip.2022.3149230

Cited by 12 publications

(26 citation statements)

References 55 publications

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“…Spatial domain methodology has seen an increasing research attention and dominant performance improvement from PDEbased methods [3], [10], [34] and TV [4], [35], [36] methods. PDE-based methods mainly apply the anisotropic diffusion (AD) [3], [10], [34] under the guidance of diffusion coefficients [34], encouraging internal diffusion in similar regions and inhabiting interaction between different regions. The diffusion coefficient consists of three parts, edge information, noise information and a non-negative decreasing function, keeping a balance between edge preserving and noise removal.…”

Section: Edge Informationmentioning

confidence: 99%

“…S PECKLE noise is a kind of structure-dependent or spatiodependent noise and inherently exists in ultrasound, synthetic aperture radar or laser imaging [4]- [9], lowering image contrast, obscuring object boundaries, and degrading image segmentation performance. Many despeckling methods [4]- [6], [10]- [12] have been developed to reduce the speckle effect with more or less structure detail preservation and improve traditional image segmentation performance. Deep learning is a newly popular image segmentation method [13], [14], greatly surpassing traditional methods in performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Is despeckling necessary for deep learning based ultrasound image segmentation?

Liu¹,

Chen²,

Qin³

et al. 2023

Preprint

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show abstract

Section: Edge Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Is despeckling necessary for deep learning based ultrasound image segmentation?

Liu¹,

Chen²,

Qin³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Spatial domain methodology has seen an increasing research attention and dominant performance improvement from PDEbased methods [3], [8], [32] and TV [4], [33], [34] methods. PDE-based methods mainly apply the anisotropic diffusion (AD) [3], [8], [32] under the guidance of diffusion coefficients [32], encouraging internal diffusion in similar regions and inhabiting interaction between different regions. The diffusion coefficient consists of three parts, edge information, noise information and a non-negative decreasing function, keeping a balance between edge preserving and noise removal.…”

Section: Edge Informationmentioning

confidence: 99%

“…The edge information and noise information of classical AD methods has been shown in Table I. Sudeb et al [8] represented edge information in another PDE-based method, which injected the past edge information into the diffusion process and preserved fine features. TV based methods mainly focus on fidelity term and regularization term [34] to use smooth and regularization prior.…”

Section: Edge Informationmentioning

confidence: 99%

“…S PECKLE noise [4]- [7] is a kind of structure-dependent or spatio-dependent noise and inherently exists in ultrasound, synthetic aperture radar or laser imaging, lowering image contrast, obscuring object boundaries, and degrading image segmentation performance. Many despeckling methods [4]- [6], [8]- [10] have been developed to reduce the speckle effect with more or less structure detail preservation and improve traditional image segmentation performance. Deep learning is a newly popular image segmentation method [11], [12], greatly surpassing traditional methods in performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Is denoising necessary for ultrasound image segmentation deep learning: review and benchmark

Liu¹,

Chen²,

Qin³

et al. 2023

Preprint

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Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Singh

Diwakar

2023

Int J Imaging Syst Tech

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Medical diagnostics rely heavily on ultrasound imaging since it is non-invasive, inexpensive, and able to provide images in real time. However, the inherent existence of a kind of signal-dependent noise in these images diminishes the use of ultrasound imaging systems. Speckle noise is inherently present in ultrasound images. Its inherent presence occurs during the image acquisition phase. It decreases the diagnostic usefulness of this imaging modality since it interferes with and reduces the image resolution and contrast. Speckle noise is multiplicative in nature. This multiplicative nature of speckle noise causes adverse effects on ultrasound imaging because it distorts the image quality and causes a loss of the patient's informative content in ultrasound imaging. This causes difficulty in tissue characterization. A lot of research has been done in this field to remove speckle noise while preserving medical information in the image. To handle this issue, research has been grouped into multiple domains. The two main domains include homomorphic and non-homomorphic filtering. The homomorphic filtering uses a logarithmic transform that converts this multiplicative nature into an additive nature and uses any additive image restoration model to do a despeckling task. Non-homomorphic filtering encompasses all methods that do not employ a logarithmic transform. Since the presence of speckle noise in ultrasound imaging is an inherent property. Therefore, the despeckling of medical ultrasound images is a mandatory task that cannot be avoided or ignored. This paper proposes a two-step hybrid and homomorphic despeckling technique, where modified total variation method is applied as step one for speckle reduction purposes. Step two implements method of noise thresholding in the non-subsampled contourlet transform (NSCT) domain using the bivariate shrinkage rule for edge preservation. The effectiveness and reliability of the proposed method are tested by comparing it with some of the latest methods based on qualitative and quantitative analyses. This paper will help the new researchers understand various problems and their solutions related to ultrasound image despeckling.

show abstract

A New Non-Linear Hyperbolic-Parabolic Coupled PDE Model for Image Despeckling

Cited by 12 publications

References 55 publications

Is despeckling necessary for deep learning based ultrasound image segmentation?

Is despeckling necessary for deep learning based ultrasound image segmentation?

Is denoising necessary for ultrasound image segmentation deep learning: review and benchmark

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

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