Abstract:
Single-pixel imaging is a quantum-inspired imaging technique that uses a single photodetector instead of a traditional multi-pixel matrix sensor, similarly to the well-known quantum ghost imaging. In contrast to ghost imaging, single-pixel imaging does not require entangled photons, but relies on spatial modulation of light (a sequence of light patterns). A single-pixel camera forms an image by measuring the integral overlap of an object and a light pattern, and the image is reconstructed from a set of such measurements using special computational algorithms. This method allows to obtain images in modes that are unattainable or difficult to achieve with standard cameras. For example, it becomes possible to perform single-photon detection, detect in spectral ranges outside the sensitivity range of silicon (infrared, terahertz, gamma), and obtain images of objects outside the line of sight or in highly scattering media. In the talk, I discuss methods for computing images in single-pixel imaging based on compressed sampling and machine learning, as well as their experimental implementation.
