Nonlinear Reconstruction of Images from Patterns Generated by Deterministic or Random Optical Masks-Concepts and Review of Research.

Indirect-imaging methods involve at least two steps, namely optical recording and computational reconstruction. The optical-recording process uses an optical modulator that transforms the light from the object into a typical intensity distribution. This distribution is numerically processed to reconstruct the object's image corresponding to different spatial and spectral dimensions. There have been numerous optical-modulation functions and reconstruction methods developed in the past few years for different applications. In most cases, a compatible pair of the optical-modulation function and reconstruction method gives optimal performance. A new reconstruction method, termed nonlinear reconstruction (NLR), was developed in 2017 to reconstruct the object image in the case of optical-scattering modulators. Over the years, it has been revealed that the NLR can reconstruct an object's image modulated by an axicons, bifocal lenses and even exotic spiral diffractive elements, which generate deterministic optical fields. Apparently, NLR seems to be a universal reconstruction method for indirect imaging. In this review, the performance of NLR isinvestigated for many deterministic and stochastic optical fields. Simulation and experimental results for different cases are presented and discussed.

Action Recognition Using Single-Pixel Time-of-Flight Detection.

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject's privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network.

Endlessly single-mode photonic crystal fiber as a high resolution probe.

We sample ultra-broadband light, focused onto a diffraction-limited spot, to an endlessly single-mode photonic crystal fiber (ESM) and detect both the field amplitude and phase using a SEA TADPOLE interferometer. We resolve spatial features up to 2.5 times finer than the fiber mode size while sampling the periodic features of the bipolar oscillating field in the transverse section. The resolution enhancement is expected also in other types of single-mode fibers in intensity measurements and leads to an inexpensive method for characterizing the point-spread function of such optical fields, e.g., diffraction-limited spots from microscope objectives. In addition, we demonstrate the guidance of a high-NA light field in the fine structure of an ESM fiber mode. The results are especially valuable for devices where a fiber tip acts as an input slit and defines the spatial resolution, e.g., fiber-based interferometers, spectrometers, and sensors.