Fourth order PDE based ultrasound despeckling using ENI classification

Soorajkumar, R.; Kumar, Pankaj; Girish, Deeptha; Rajan, Jeny

doi:10.1109/spcom.2016.7746633

2016 International Conference on Signal Processing and Communications (SPCOM) 2016

DOI: 10.1109/spcom.2016.7746633

|View full text |Cite

Fourth order PDE based ultrasound despeckling using ENI classification

R. Soorajkumar

Pankaj Kumar

Deeptha Girish³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

2021

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the rapid development of sensors in the last few years has brought us new opportunities, resulting in the birth of optical-driven non-local SAR speckle removal technology. Secondly, the popularity of PDE-based methods [42,32,31] relies on nonlinear diffusion techniques. The second order equation usually has the extremum principle to ensure the stability and smoothness of the restoration results.…”

mentioning

confidence: 99%

An adaptive total variational despeckling model based on gray level indicator frame

Li¹,

Guo²,

Wu³

2021

IPI

View full text Add to dashboard Cite

For the characteristics of the degraded images with multiplicative noise, the gray level indicators for constructing adaptive total variation are proposed. Based on the new regularization term, we propose the new convex adaptive variational model. Then, considering the existence, uniqueness and comparison principle of the minimizer of the functional. The finite difference method with rescaling technique and the primal-dual method with adaptive step size are used to solve the minimization problem. The paper ends with a report on numerical tests for the denoising of images subject to multiplicative noise, the comparison with other methods is provided as well.

show abstract

mentioning

confidence: 99%

An adaptive total variational despeckling model based on gray level indicator frame

Li¹,

Guo²,

Wu³

2021

IPI

View full text Add to dashboard Cite

show abstract

Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE

Wang

Zhu

Ling

2019

SIViP

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Fourth order PDE based ultrasound despeckling using ENI classification

Cited by 2 publications

References 25 publications

An adaptive total variational despeckling model based on gray level indicator frame

An adaptive total variational despeckling model based on gray level indicator frame

Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE

Contact Info

Product

Resources

About