  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.
