Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 14,
  • Issue 9,
  • pp. 091701-
  • (2016)

Sparse photoacoustic microscopy based on low-rank matrix approximation

Not Accessible

Your library or personal account may give you access

Abstract

As a high-resulotion biological imaging technology, photoacoustic microscopy (PAM) is difficult to use in real-time imaging due to the long data acquisition time. Herein, a fast data acquisition and image recovery method named sparse PAM based on a low-rank matrix approximation is proposed. Specifically, the process to recover the final image from incomplete data is formulated into a low-rank matrix completion framework, and the “Go Decomposition” algorithm is utilized to solve the problem. Finally, both simulated and real PAM experiments are conducted to verify the performance of the proposed method and demonstrate clinical potential for many biological diseases.

© 2016 Chinese Laser Press

PDF Article
More Like This
Joint sparse and low rank recovery algorithm for compressive hyperspectral imaging

Tatiana Gelvez, Hoover Rueda, and Henry Arguello
Appl. Opt. 56(24) 6785-6795 (2017)

Unsupervised change detection of satellite images using low rank matrix completion

Shibo Gao, Yongmei Cheng, and Yongqiang Zhao
Opt. Lett. 38(23) 5146-5149 (2013)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved