In scientific and industrial research, three-dimensional (3D) imaging, or depth measurement, is a critical tool that provides detailed insight into surface properties. Confocal microscopy, known for its precision in surface measurements, plays a key role in this field. However, 3D imaging based on confocal microscopy is often challenged by significant data requirements and slow measurement speeds. In this paper, we present a novel self-supervised learning algorithm called SSL Depth that overcomes these challenges. Specifically, our method exploits the feature learning capabilities of neural networks while avoiding the need for labeled data sets typically associated with supervised learning approaches. Through practical demonstrations on a commercially available confocal microscope, we find that our method not only maintains higher quality, but also significantly reduces the frequency of the z-axis sampling required for 3D imaging. This reduction results in a remarkable 16× measurement speed, with the potential for further acceleration in the future. Our methodological advance enables highly efficient and accurate 3D surface reconstructions, thereby expanding the potential applications of confocal microscopy in various scientific and industrial fields.

Single-pixel imaging (SPI) captures two-dimensional images utilizing a sequence of modulation patterns and measurements recorded by a single-pixel detector. However, the sequential measurement of a scene is time-consuming, especially for high-spatial-resolution imaging. Furthermore, for spectral SPI, the enormous data storage and processing time requirements substantially diminish imaging efficiency. To reduce the required number of patterns, we propose a strategy by optimizing a Hadamard pattern sequence via Morton frequency domain scanning to enhance the quality of a reconstructed spectral cube at low sampling rates. Additionally, we expedite spectral cube reconstruction, eliminating the necessity for a large Hadamard matrix. We demonstrate the effectiveness of our approach through both simulation and experiment, achieving sub-Nyquist sampling of a three-dimensional spectral cube with a spatial resolution of 256 × 256 pixels and 181 spectral bands and a reduction in reconstruction time by four orders of magnitude. Consequently, our method offers an efficient solution for compressed spectral imaging.

Ghost imaging has been attracting more and more attention, which provides a way to obtain images of objects with only a single-pixel detector. Considering possible applications, it becomes urgent to clarify the sensitivity of ghost imaging. Due to the unique characteristics of single-pixel detectors, which collect photons without distributing them to multiple pixels, often outperforming array sensors, ghost imaging is believed to be more sensitive than conventional imaging. However, a systematic analysis on the sensitivity of ghost imaging is yet to be completed. In this paper, we present a method for quantitatively assessing the sensitivity of ghost imaging. A detailed comparison is provided between ghost imaging and conventional imaging, taking into account the particle nature of photons and the noise of detection. With the settings of the two imaging methods being the same to the most extent, the minimal required number of detected photons for images of a certain quality is considered. For the thermal source version, ghost imaging demonstrates enhanced sensitivity under practical situations, with noise considered. Employing an entangled source, ghost imaging surpasses conventional imaging techniques in terms of sensitivity obviously. In one word, ghost imaging promises higher sensitivity at low photon flux and noisy situations.

We proposed a hybrid imaging scheme to estimate a high-resolution absolute depth map from low photon counts. It leverages measurements of photon arrival times from a single-photon LiDAR and an intensity image from a conventional high-resolution camera. Using a tailored fusion algorithm, we jointly processed the raw measurements from both sensors and output a high-resolution absolute depth map. We scaled up the resolution by a factor of 10, achieving 1300 × 2611 pixels and extending ∼4.7 times the unambiguous range. These results demonstrated the superior capability of long-range high-resolution 3D imaging without range ambiguity.

High-sensitivity radio-frequency optically pumped magnetometers (RF-OPMs), working without cryogenic condition, play a critical role in magnetic field imaging (MFI) at low frequencies (e.g., 100 Hz to 1 MHz). We introduce the principle of operation and recent developments of RF-OPMs and focus on reviewing the MFI applications in magnetic induction tomography, ultralow-field magnetic resonance imaging, and magnetic particle imaging. For the applications of RF-OPMs, ranging from industrial monitoring to medical imaging and security screening, the unshielded and portable RF-OPMs (and RF-OPM array) techniques are still under the further development for detecting and scanning over the target object for accomplishing the final three-dimensional imaging, and thus extremely require the abilities of active compensation of the ambient magnetic field and sensor miniaturization in the future.

Imaging objects hidden behind turbid media is of great scientific importance and practical value, which has been drawing a lot of attention recently. However, most of the scattering imaging methods rely on a narrow linewidth of light, limiting their application. A mixture of the scattering light from various spectra blurs the detected speckle pattern, bringing difficulty in phase retrieval. Image reconstruction becomes much worse for dynamic objects due to short exposure times. We here investigate non-invasively recovering images of dynamic objects under white-light irradiation with the multi-frame OTF retrieval engine (MORE). By exploiting redundant information from multiple measurements, MORE recovers the phases of the optical-transfer-function (OTF) instead of recovering a single image of an object. Furthermore, we introduce the number of non-zero pixels (NNP) into MORE, which brings improvement on recovered images. An experimental proof is performed for dynamic objects at a frame rate of 20 Hz under white-light irradiation of more than 300 nm bandwidth.

We establish a quantum theory of computational ghost imaging and propose quantum projection imaging where object information can be reconstructed by quantum statistical correlation between a certain photon number of a bucket signal and digital micromirror device random patterns. The reconstructed image can be negative or positive, depending on the chosen photon number. In particular, the vacuum state (zero-number) projection produces a negative image with better visibility and contrast-to-noise ratio. The experimental results of quantum projection imaging agree well with theoretical simulations and show that, under the same measurement condition, vacuum projection imaging is superior to conventional and fast first-photon ghost imaging in low-light illumination.

Performance assessment of an imaging system is important for the optimization design with various technologies. The information-theoretic viewpoint based on communication theory or statistical inference theory can provide objective and operational measures on imaging performance. These approaches can be further developed by combining with the quantum statistical inference theory for optimizing imaging performance over measurements and analyze its quantum limits, which is demanded in order to improve an imaging system when the photon shot noise in the measurement is the dominant noise source. The aim of this review is to discuss and analyze the recent developments in this branch of quantum imaging.

Microscopes are indispensable tools in modern biology and medicine. With the development of microscopy, the signal-to-noise ratio of microscopes is now limited by the shot noise. Recently, quantum-enhanced microscopic imaging provides a feasible approach for improving the signal-to-noise ratio since it can beat the shot-noise limit by using quantum light. In this review, we first briefly introduce quantum states applied in quantum-enhanced microscopic imaging, and then we provide an overview of the principle and progress of quantum-enhanced stimulated Raman scattering microscopy, entangled two-photon microscopy, and quantum-enhanced differential interference contrast microscopy.

Since its first experimental demonstration, “ghost imaging” has attracted much attention, perhaps not only because of its interesting physics but also because of its attractive application. This review article discusses the physics and application of ghost imaging: (1) emphasizes the nonlocal two-photon interference nature of ghost imaging, including detailed analysis and calculations; (2) introduces three types of applications with unique advantages of ghost imaging, including a light detection and ranging device with imaging ability, namely, an Imaging Lidar or ILidar system; a turbulence-resistant, or turbulence-free, imaging technology; and a vibration-resistant X-ray microscope of high resolving capability. This article is prepared for a Special Issue of Chinese Optics Letters, intended for general audiences, especially young researchers and students who are interested in ghost imaging.