Deep Learning-Based Super-Resolution Acoustic Holography for Phased Transducer Array

doi:10.1063/5.0223530

	Deep Learning-Based Super-Resolution Acoustic Holography for Phased Transducer Array
	Qingyi Lu, Chengxi Zhong, Qing Liu, Hu Su, Song Liu
	2024-09
发表期刊	JOURNAL OF APPLIED PHYSICS (IF:2.7[JCR-2023],2.6[5-Year])
ISSN	0021-8979
EISSN	1089-7550
卷号	136 期号:13
发表状态	正式接收
DOI	10.1063/5.0223530
摘要	Acoustic holography (AH) is a technique with significant potential in realms, such as biomedicine, industry, and augmented reality. The implementation of acoustic holograms can be realized by a passive approach or active ones. Although the passive approach (by a 3D printer) can achieve high-quality acoustic field generation, it is constrained by high manufacturing costs and static field control. On the contrary, the active approach with a phased transducer array (PTA) as the latest technique stands out since it supports dynamic, flexible, and reconfigurable acoustic field generation. However, current PTA-based AH techniques face the drawback of inferior acoustic field fineness due to the Spatial Bandwidth Product (SBP) limit of PTA, which hinders the application of PTA in precise tasks, such as neural electrodes and microfluidics control. To address this issue, we propose a super-resolution acoustic holography (SRAH) method inspired by the concept of super-resolution in ultrasonic imaging and computer vision, by which we can generate acoustic fields reaching the physical diffraction limit of acoustic waves regardless SBP of PTA. In other words, this method enables high-SBP acoustic field generation with low-SBP PTA. The method is based on self-supervised learning, integrating a generative adversarial network and a physical model of acoustic wave propagation, specifically the linear accumulation method. Both simulation and experimental results demonstrate that the proposed method can generate high-fidelity acoustic fields suitable for intricate tasks with low-SBP PTA. Moreover, the performance of the algorithm improves as the target SBP increases. Therefore, the proposed SRAH method shows great potential for applications requiring elaborate manipulation. © 2024 Author(s).
关键词	Acoustic fields Acoustic transducers Acoustic wave diffraction Holograms Lithography Microfluidics Sound recording Ultrasonic imaging Bandwidth product Cost fields Field generations High quality Learning-based super-resolution Manufacturing cost Spatial bandwidth Static fields Superresolution Transducer array
收录类别	EI
语种	英语
出版者	American Institute of Physics
EI入藏号	20244217187406
EI主题词	Acoustic holography
EI分类号	1401.4.1 ; 743 Holography ; 745.1 Printing ; 746 Imaging Techniques ; 751.1 Acoustic Waves ; 752.1 Acoustic Devices ; 752.2 Sound Recording ; 753.3 Ultrasonic Applications
原始文献类型	Journal article (JA)
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/421769
专题	信息科学与技术学院_硕士生
作者单位	Shanghaitech University
第一作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Qingyi Lu, Chengxi Zhong, Qing Liu, Hu Su, Song Liu. Deep Learning-Based Super-Resolution Acoustic Holography for Phased Transducer Array[J]. JOURNAL OF APPLIED PHYSICS,2024,136(13).
APA	Qingyi Lu, Chengxi Zhong, Qing Liu, Hu Su, Song Liu.(2024).Deep Learning-Based Super-Resolution Acoustic Holography for Phased Transducer Array.JOURNAL OF APPLIED PHYSICS,136(13).
MLA	Qingyi Lu, Chengxi Zhong, Qing Liu, Hu Su, Song Liu."Deep Learning-Based Super-Resolution Acoustic Holography for Phased Transducer Array".JOURNAL OF APPLIED PHYSICS 136.13(2024).