2020
DOI: 10.1109/tuffc.2020.2977942
|View full text |Cite
|
Sign up to set email alerts
|

A Mixed Transmitting–Receiving Beamformer With a Robust Generalized Coherence Factor: Enhanced Resolution and Contrast

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
5
1

Relationship

1
5

Authors

Journals

citations
Cited by 7 publications
(4 citation statements)
references
References 28 publications
0
4
0
Order By: Relevance
“…Wang et al 31 proposed MTR in combination with Generalized Coherence Factor (GCF) to enhance the contrast properties of the images. They mentioned that various GCF-weighted images could be obtained by data compounding on the transmit or receive aperture and for various compounding lengths.…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…Wang et al 31 proposed MTR in combination with Generalized Coherence Factor (GCF) to enhance the contrast properties of the images. They mentioned that various GCF-weighted images could be obtained by data compounding on the transmit or receive aperture and for various compounding lengths.…”
Section: Discussionmentioning
confidence: 99%
“…MTR beamforming: In MTR beamforming method, the covariance matrices are calculated similarly to the JTR method, with two significant differences for calculating the output. The first is that the transmitting and receiving weight vectors are determined by the following equations 31 :…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Zhao et al proposed the joint transmitting-receiving (JTR) MV beamformer, in which two MV weights are calculated to obtain the improvement of the resolution [8]. A mixed transmitting-receiving (MTR) proposed by Wang et al further enhanced the imaging quality by redefining the MV optimization problem [9]. Nguyen et al proposed a spatial coherence approach to implement the MV beamformer using datacompounded methods [10].…”
Section: Introductionmentioning
confidence: 99%