  The quality of the reconstructed photoacoustic image largely depends on the
amount of photoacoustic (PA) boundary data available, which in turn is
proportional to the number of detectors employed. In case of limited data
(owing to less number of detectors due to cost/instrumentation constraints),
the reconstructed PA images suffer from artifacts and are often noisy. In this
work, for the first time, a deep learning based model was developed to super
resolve and denoise the photoacoustic sinogram data. The proposed method was
compared with existing nearest neighbor interpolation and wavelet based
denoising techniques and was shown to outperform them both in numerical and
in-vivo cases. The improvement obtained in Root Mean Square Error (RMSE) and
Peak Signal to Noise Ratio (PSNR) for the reconstructed PA image using the
sinogram data that was super-resolved and denoised using proposed neural
network based method was as high as 41.70 % and 6.93 dB respectively compared
to utilizing limited sinogram data.
