  Low fluence illumination sources can facilitate clinical transition of
photoacoustic imaging because they are rugged, portable, affordable, and safe.
However, these sources also decrease image quality due to their low fluence.
Here, we propose a denoising method using a multi-level wavelet-convolutional
neural network to map low fluence illumination source images to its
corresponding high fluence excitation map. Quantitative and qualitative results
show a significant potential to remove the background noise and preserve the
structures of target. Substantial improvements up to 2.20, 2.25, and 4.3-fold
for PSNR, SSIM, and CNR metrics were observed, respectively. We also observed
enhanced contrast (up to 1.76-fold) in an in vivo application using our
proposed methods. We suggest that this tool can improve the value of such
sources in photoacoustic imaging.
