We present a single-layer optical inference system based on nonlinear optical diffraction. In this system, input optical data is superposed with control parameters and then focused into a nonlinear optical crystal leading to second harmonic generation which serves as the output of the system. We demonstrate image classification while utilizing a very low number of degrees of freedom. Importantly, the performance of the system can be tuned by controlling the level of spatial mode mixing in the nonlinear crystal.
The vast majority of medical endoscopes used today are based on optical fibers. Although most endoscopes are used to retrieve the image of the object at the distal end of the fiber endoscope, recent developments enable imaging the surrounding of the endoscope, such as swallowable tethered capsules. However, these capsules are much thicker than the optical fiber itself, and require mechanical rotations in order to scan the surrounding of the capsule. Hence, the ability to image the surrounding of a standard single-core fiber will be a major improvement to the current capabilities since it will allow for a much more convenient and possibly faster operation of the device, and will enable reaching places that are unreachable using the currently available technology. A mechanism that may enable such ability is the Rayleigh scattering that is present in standard optical fibers, and causes scattering of light that propagates inside the fiber in all directions. In this work we discuss two tasks we have recently investigated towards imaging the surrounding of a standard step-index multi-mode fiber. The first one is retrieving visual data that was input to the fiber based on Rayleigh side-scattered light using deep learning. The second one is focusing of Rayleigh side-scattered light using wave-front shaping, which is a possible means to overcome the very low intensity of Rayleigh scattering.
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