  Structured light beams, in particular those carrying orbital angular momentum
(OAM), have gained a lot of attention due to their potential for enlarging the
transmission capabilities of communication systems. However, the use of
OAM-carrying light in communications faces two major problems, namely
distortions introduced during propagation in disordered media, such as the
atmosphere or optical fibers, and the large divergence that high-order OAM
modes experience. While the use of non-orthogonal modes may offer a way to
circumvent the divergence of high-order OAM fields, artificial intelligence
(AI) algorithms have shown promise for solving the mode-distortion issue.
Unfortunately, current AI-based algorithms make use of large-amount
data-handling protocols that generally lead to large processing time and high
power consumption. Here we show that a low-power, low-cost image sensor can
itself act as an artificial neural network that simultaneously detects and
reconstructs distorted OAM-carrying beams. We demonstrate the capabilities of
our device by reconstructing (with a 95$\%$ efficiency) individual Vortex,
Laguerre-Gaussian (LG) and Bessel modes, as well as hybrid (non-orthogonal)
coherent superpositions of such modes. Our work provides a potentially useful
basis for the development of low-power-consumption, light-based communication
devices.
