  Although many exoplanets have been indirectly detected over the last years,
direct imaging of them with ground-based telescopes remains challenging. In the
presence of atmospheric fluctuations, it is ambitious to resolve the high
brightness contrasts at the small angular separation between the star and its
potential partners. Post-processing of telescope images has become an essential
tool to improve the resolvable contrast ratios. This paper contributes a
post-processing algorithm for fast-cadence imaging, which deconvolves sequences
of telescope images. The algorithm infers a Bayesian estimate of the
astronomical object as well as the atmospheric optical path length, including
its spatial and temporal structures. For this, we utilize physics-inspired
models for the object, the atmosphere, and the telescope. The algorithm is
computationally expensive but allows to resolve high contrast ratios despite
short observation times and no field rotation. We test the performance of the
algorithm with point-like companions synthetically injected into a real data
set acquired with the SHARK-VIS pathfinder instrument at the LBT telescope.
Sources with brightness ratios down to $6\cdot10^{-4}$ to the star are detected
at $185$ mas separation with a short observation time of $0.6\,\text{s}$.
