  Scattering of light in complex media scrambles optical wavefronts and breaks
the principles of conventional imaging methods. For decades, researchers have
endeavored to conquer the problem by inventing approaches such as adaptive
optics, iterative wavefront shaping, and transmission matrix measurement. That
said, imaging through/into thick scattering media remains challenging to date.
With the rapid development of computing power, deep learning has been
introduced and shown potentials to reconstruct target information through
complex media or from rough surfaces. But it also fails once coming to
optically thick media where ballistic photons become negligible. Here, instead
of treating deep learning only as an image extraction method, whose
best-selling advantage is to avoid complicate physical models, we exploit it as
a tool to explore the underlying physical principles. By adjusting the weights
of ballistic and scattered photons through a random phasemask, it is found that
although deep learning can extract images from both scattered and ballistic
light, the mechanisms are different: scattering may function as an encryption
key and decryption from scattered light is key sensitive, while extraction from
ballistic light is stable. Based on this finding, it is hypothesized and
experimentally confirmed that the foundation of the generalization capability
of trained neural networks for different diffusers can trace back to the
contribution of ballistic photons, even though their weights of photon counting
in detection are not that significant. Moreover, the study may pave an avenue
for using deep learning as a probe in exploring the unknown physical principles
in various fields.
