  In deep tissue photoacoustic imaging the spatial resolution is inherently
limited by the acoustic wavelength. Recently, it was demonstrated that it is
possible to surpass the acoustic diffraction limit by analyzing fluctuations in
a set of photoacoustic images obtained under unknown speckle illumination
patterns. Here, we purpose an approach to boost reconstruction fidelity and
resolution, while reducing the number of acquired images by utilizing a
compressed sensing computational reconstruction framework. The approach takes
into account prior knowledge of the system response and sparsity of the target
structure. We provide proof of principle experiments of the approach and
demonstrate that improved performance is obtained when both speckle
fluctuations and object priors are used. We numerically study the expected
performance as a function of the measurements signal to noise ratio and sample
spatial-sparsity. The presented reconstruction framework can be applied to
analyze existing photoacoustic experimental datasets containing dynamic
fluctuations.
