3D imaging at THz frequencies based on 2D fundamental and sub-harmonic heterodyne detection of the spatial Fourier spectrum

点击翻译按钮获取中文摘要

Fourier imaging is a free-space, digital imaging modality. It is based on the illumination of a scene by coherent electromagnetic radiation and the recording of the radiation transmitted through an optical system (“lens”) in the backside focal plane. There, the field distribution represents the scene’s spatial Fourier spectrum modified by a propagation term, which contains the information about the object distances. Fourier imaging lends itself especially well for the terahertz (THz) frequency regime where the diffraction limitation forces one to work with a limited number of detector pixels. Fourier imaging covers a large field-of-view. Together with the fact that the field distribution in the focal plane is compact, this is beneficial to obtain information about a whole scene even if only a low number of pixels is available. Furthermore, the spatial resolution can be selected by how far out in the focal plane the high Fourier components are measured. Most importantly, Fourier imaging enables the computation of three-dimensional images, if amplitude and phase of the field distribution in the two-dimensional focal plane are recorded. We have demonstrated this capability recently at 0.3 THz with a transistor-based TeraFET detector, developed in our group and operated in heterodyne mode. In order to improve the spatial resolution, we now extend this research to 0.6 THz, using sub-harmonic heterodyne detection. The achievable depth resolution will be determined. As our prior work has identified phase distortion problems by standing-wave effects, devices for their suppression will be developed. These include both a diffuser for a reduction of the radiation's coherence length, and a THz optical isolator based on a metamaterial polarization converter and polarization filtering. An intriguing aspect of directly measuring the Fourier spectra of scenes, is, that one can take advantage of the vast progress achieved in the field of imaging processing and storage, where Fourier transformation often lies at the core of the processing concepts. A property of Fourier spectra is that they are often sparse. This has led to compressed detection schemes, where only a limited number of Fourier components is needed for a good image fidelity. We will explore the potential of compressed sensing with selected focal-plane detection points for simple three-dimensional scenes (objects at different distances). Another advantage of Fourier imaging is its close relationship with holographic imaging techniques. For these, deep learning approaches of pattern recognition have brought impressive progress with regard to a robust phase recovery, which has enabled an enhancement of the depth-of-field as well as super-resolution. These advances appear rather straightforward to extend to coherent THz Fourier imaging. In a collaboration with a team of experts on deep learning, we will explore ways to make such deep-learning approaches applicable to THz Fourier imaging.