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Optical detectors with single-photon sensitivity and large dynamic range would facilitate a variety of applications. Specifically, the capability of extending operation wavelengths into the mid-infrared region is highly attractive. Here we implement a mid-infrared frequency upconversion detector for counting and resolving photons at 3 μm. Thanks to the spectrotemporal engineering of the involved optical fields, the mid-infrared photons could be spectrally translated into the visible band with a conversion efficiency of 80%. In combination with a silicon avalanche photodiode, we obtained unprecedented performance with a high overall detection efficiency of 37% and a low noise equivalent power of 1.8 × 10 − 17    W / Hz 1 / 2 . Furthermore, photon-number-resolving detection at mid-infrared wavelengths was demonstrated, for the first time to our knowledge, with a multipixel photon counter. The implemented upconversion detector exhibited a maximal resolving photon number up to 9 with a noise probability per pulse of 0.14% at the peak detection efficiency. The achieved photon counting and resolving performance might open up new possibilities in trace molecule spectroscopy, sensitive biochemical sensing, and free-space communications, among others.

Single-pixel cameras have recently emerged as promising alternatives to multi-pixel sensors due to reduced costs and superior durability, which are particularly attractive for mid-infrared (MIR) imaging pertinent to applications including industry inspection and biomedical diagnosis. To date, MIR single-pixel photon-sparse imaging has yet been realized, which urgently calls for high-sensitivity optical detectors and high-fidelity spatial modulators. Here, we demonstrate a MIR single-photon computational imaging with a single-element silicon detector. The underlying methodology relies on nonlinear structured detection, where encoded time-varying pump patterns are optically imprinted onto a MIR object image through sum-frequency generation. Simultaneously, the MIR radiation is spectrally translated into the visible region, thus permitting infrared single-photon upconversion detection. Then, the use of advanced algorithms of compressed sensing and deep learning allows us to reconstruct MIR images under sub-Nyquist sampling and photon-starving illumination. The presented paradigm of single-pixel upconversion imaging is featured with single-pixel simplicity, single-photon sensitivity, and room-temperature operation, which would establish a new path for sensitive imaging at longer infrared wavelengths or terahertz frequencies, where high-sensitivity photon counters and high-fidelity spatial modulators are typically hard to access.

Edge enhanced imaging via the spiral phase contrast enables to reveal the phase or amplitude gradients of a target, which has been proved useful in feature recognition, machine vision, and object identification. A long quest is to extend the operation wavelength into the mid‐infrared (MIR) region, as highly demanded in various fields including infrared sensing, astronomic observation, and biomedical diagnosis. Here, ultra‐sensitive MIR imaging at the single‐photon level based on nonlinear frequency upconversion is demonstrated, where the spectrally converted replica of the MIR object image at 3070 nm is captured by a silicon electron multiplying charged coupled device. The imaging sensitivity is significantly improved by the coincidence pulsed pumping with a spectro‐temporal optimization. Furthermore, the edge enhancement is realized by imprinting the spiral phase pattern of the pump onto the upconverted field at the Fourier plane within the nonlinear crystal. Such a nonlinear spatial filter not only provides an effective way to implement the required high‐fidelity vortex screening in the edge enhanced detection, but also renders the MIR illumination into a visible image in an efficient and low‐noise fashion. The presented system for MIR edge enhanced imaging might facilitate immediate applications in label‐free histopathological diagnosis and non‐destructive defect inspection.

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