  Strong scattering medium brings great difficulties to optical imaging, which
is also a problem in medical imaging and many other fields. Optical memory
effect makes it possible to image through strong random scattering medium.
However, this method also has the limitation of limited angle field-of-view
(FOV), which prevents it from being applied in practice. In this paper, a kind
of practical convolutional neural network called PDSNet is proposed, which
effectively breaks through the limitation of optical memory effect on FOV.
Experiments is conducted to prove that the scattered pattern can be
reconstructed accurately in real-time by PDSNet, and it is widely applicable to
retrieve complex objects of random scales and different scattering media.
