Jointly Learning Selection Matrices for Transmitters, Receivers and Fourier Coefficients in Multichannel Imaging

Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling (J-DPS) method, which aims to learn optimal selection matrices sim...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 8691 - 8695
Main Authors Wang, Han, Zhou, Yiming, Perez, Eduardo, Romer, Florian
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.04.2024
Subjects
Compressed Sensing
CVNNs
Deep Learning
Image coding
Multichannel Imaging
Receivers
Signal processing algorithms
Sparse Signal Recovery
Transmitters
Ultrasonic imaging
Ultrasonic variables measurement
Weight measurement
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ISSN2379-190X
DOI10.1109/ICASSP48485.2024.10448087

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Summary:Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling (J-DPS) method, which aims to learn optimal selection matrices simultaneously for transmitters, receivers, and Fourier coefficients. This task-based algorithm is realized by introducing a specialized measurement model and integrating a customized Complex Learned FISTA (CL-FISTA) network. We propose a parallel network architecture, partitioned into three segments corresponding to the three matrices, all working toward a shared optimization objective with adjustable loss allocation. A synthetic dataset is designed to reflect practical scenarios, and we provide quantitative comparisons with a traditional CRB-based algorithm, standard DPS, and J-DPS.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10448087