Autoencoder based blind source separation for photoacoustic resolution enhancement

Matan Benyamin, Hadar Genish, Ran Califa, Lauren Wolbromsky, Michal Ganani, Zhen Wang, Shuyun Zhou, Zheng Xie, Zeev Zalevsky

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Photoacoustics is a promising technique for in-depth imaging of biological tissues. However, the lateral resolution of photoacoustic imaging is limited by size of the optical excitation spot, and therefore by light diffraction and scattering. Several super-resolution approaches, among which methods based on localization of labels and particles, have been suggested, presenting promising but limited solutions. This work demonstrates a novel concept for extended-resolution imaging based on separation and localization of multiple sub-pixel absorbers, each characterized by a distinct acoustic response. Sparse autoencoder algorithm is used to blindly decompose the acoustic signal into its various sources and resolve sub-pixel features. This method can be used independently or as a combination with other super-resolution techniques to gain further resolution enhancement and may also be extended to other imaging schemes. In this paper, the general idea is presented in details and experimentally demonstrated.

Original languageEnglish
Article number21414
JournalScientific Reports
Issue number1
StatePublished - Dec 2020

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (NSFC, No. 21875267, 51561145004) and the President’s International Fellowship Initiative of the Chinese Academy of Sciences (PIFI2019VMA0053).

Publisher Copyright:
© 2020, The Author(s).


Dive into the research topics of 'Autoencoder based blind source separation for photoacoustic resolution enhancement'. Together they form a unique fingerprint.

Cite this