Image Denoising Method Based on Weighted Total Variational Model with Edge Operator

Zhang, Hong; Zhou, Xiaoli; Zhan, Weixiao; Yu, Fuhua

doi:10.1007/978-3-030-03766-6_82

Cited by 1 publication

(2 citation statements)

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“…Using mean of the absolute derivatives, a new directional weighted function is proposed as given below:

α goodbreak= \frac{1}{N} \sum_{P'}^{N} \frac{C ((), p) + γ}{C ((), p^{'}) + γ}

where

C ((), p)

is Canny edge detection parameter, and

γ

is a control parameter that represents a diagonal weight factor that dominates the diagonal 52 . The Canny edge detector is an inter‐computational edge detection operator that yields sharp edges across a wide range of image edges by utilizing noise reduction, gradient computation, non‐maximum suppression, a double threshold, and edge tracking using hysteresis 53 .…”

Section: Proposed Methodologymentioning

confidence: 99%

“…where C p ð Þ is Canny edge detection parameter, and γ is a control parameter that represents a diagonal weight factor that dominates the diagonal. 52 The Canny edge detector is an inter-computational edge detection operator that yields sharp edges across a wide range of image edges by utilizing noise reduction, gradient computation, nonmaximum suppression, a double threshold, and edge tracking using hysteresis. 53 These features can be found in the Canny edge detection algorithm.…”

Section: Proposed Methodologymentioning

confidence: 99%

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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.

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“…Using mean of the absolute derivatives, a new directional weighted function is proposed as given below: