  Photoacoustic imaging (PAI) is a novel medical imaging modality that uses the
advantages of the spatial resolution of ultrasound imaging and the high
contrast of pure optical imaging. Analytical algorithms are usually employed to
reconstruct the photoacoustic (PA) images as a result of their simple
implementation. However, they provide a low accurate image. Model-based (MB)
algorithms are used to improve the image quality and accuracy while a large
number of transducers and data acquisition are needed. In this paper, we have
combined the theory of compressed sensing (CS) with MB algorithms to reduce the
number of transducer. Smoothed version of L0-norm (SL0) was proposed as the
reconstruction method, and it was compared with simple iterative reconstruction
(IR) and basis pursuit. The results show that S$\ell_0$ provides a higher image
quality in comparison with other methods while a low number of transducers
were. Quantitative comparison demonstrates that, at the same condition, the SL0
leads to a peak-signal-to-noise ratio for about two times of the basis pursuit.
