  A new development in photoacoustic (PA) imaging has been the use of compact,
portable and low-cost laser diodes (LDs), but LD-based PA imaging suffers from
low signal intensity recorded by the conventional transducers. A common method
to improve signal strength is temporal averaging, which reduces frame rate and
increases laser exposure to patients. To tackle this problem, we propose a deep
learning method that will denoise the PA images before beamforming with a very
few frames, even one. We also present a deep learning method to automatically
reconstruct point sources from noisy pre-beamformed data. Finally, we employ a
strategy of combined denoising and reconstruction, which can supplement the
reconstruction algorithm for very low signal-to-noise ratio inputs.
