* Correspondence
Fei Gao, ShanghaiTech University, Huaxia Middle Road 393, 201210
Shanghai, China
Email: gaofei@shanghaitech.edu.cn
Photoacoustic imaging (PAI) has been applied to many biomedical
applications over the past decades. However, the received PA signal
usually suffers from poor signal-to-noise ratio (SNR). Conventional
solution of employing higher-power laser, or doing long-time signal
averaging, may raise the system cost, time consumption, and tissue
damage. Another strategy is de-noising algorithm design. In this paper,
we propose a new de-noising method, termed gradient-based adaptive
wavelet de-noising, which sets the energy gradient mutation point of
low-frequency wavelet components as the threshold. We conducted
simulation, ex vivo and in vivo experiments to validate the performance
of the algorithm. The quality of de-noised PA image/signal by our
proposed algorithm has improved by 20%-40%, in comparison to the
traditional signal denoising algorithms, which produces better contrast
and clearer details. The proposed de-noising method provides potential
to improve the SNR of PA signal under single-shot low-power laser
illumination for biomedical applications in vivo .