Sparse Photoacoustic Microscopy based on Low-Rank Matrix Approximation

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.