We developed a deep learning algorithm, called enhancement Unet (E-Unet), to improve the signal-to-noise ratio (SNR) of signals acquired in a photoacoustic computed microscopy (PAM) system. We tried various combination of custom loss functions which included peak-amplitude, peak-position and mean-squared signal value with Adam optimizer for training purposes. For the testing purposes, we acquired PAM data with complicated phantoms in biological tissue. The performance of the improved signals is evaluated in terms of SNR, structural similarity index (SSIM), root mean square error (RMSE) and Pearson correlation.
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