Terahertz (THz) sources, ranging from 0.1 THz to 10 THz, between microwaves and infrared waves, have important applications in spectral detection, medical imaging, communication, etc. Difference frequency generation (DFG) is an effective method for generating terahertz with the characteristics of low cost, simple structure, widely tunable range, no threshold, and room-temperature operation. This paper reviews various optical terahertz sources of difference frequency generation based on nonlinear crystals, including DFG with inorganic crystals, DFG with organic crystals, DFG with quasi-phase-matching (QPM) crystals, DFG in waveguides, cavity-enhanced DFG, and cascaded DFG. Their recent advances, as well as their advantages and disadvantages, are fully present and discussed. This review is expected to provide a comprehensive reference for researchers in this field and a quick understanding of optical THz sources of difference frequency generation with nonlinear crystals.

This paper introduces the infrared crystal phase, and provides unconventional mechanistic insights into the commonly thought “crystal phase”. The critical challenge with the obtained fidelity phase is the unwrapping process, which was addressed using self-attention mechanism deep learning infrared-band holography. This method strikes a balance between the theoretical rigor of physical models and the flexibility of data-driven approaches. Specifically, utilizing a short wavelength infrared digital holographic system and algorithm resulted in the acquisition of high-quality wrapped phases. Then, the network architecture was applied for phase unwrapping. Through demonstrative applications, static phase-type thickness variation was measured in samples. During moments of intense phase transitions, the microstructural evolution of Na₂CO₃ crystals was monitored, and the process of perovskite material film formation was observed. The results demonstrated that environmental detection noise and twin images were effectively suppressed, and phase values were also dramatically varied after stabilization of the traditional amplitude signal. These discoveries guide the characterization of novel materials and also provide insights into alterations of properties during crystal preparation and growth, which is crucial for the final outcome.

Ghost imaging using deep learning (GIDL) is a kind of computational quantum imaging method devised to improve the imaging efficiency. However, among most proposals of GIDL so far, the same set of random patterns were used in both the training and test set, leading to a decrease of the generalization ability of networks. Thus, the GIDL technique can only reconstruct the profile of the image of the object, losing most of the details. Here we optimize the simulation algorithm of ghost imaging (GI) by introducing the concept of “batch” into the pre-processing stage. It can significantly reduce the data acquisition time and create reliable simulation data. The generalization ability of GIDL has been appreciably enhanced. Furthermore, we develop a residual-based framework for the GI system, namely the double residual U-Net (DRU-Net). The imaging quality of GI has been tripled in the evaluation of the structural similarity index by our proposed DRU-Net.

A single-pixel neural network object classification scenario in the sub-Nyquist ghost imaging system is proposed. Based on the neural network, objects are classified directly by bucket measurements without reconstructing images. Classification accuracy can still be maintained at 94.23% even with only 16 measurements (less than the Nyquist limit of 1.56%). A parallel computing scheme is applied in data processing to reduce the object acquisition time significantly. Random patterns are used as illumination patterns to illuminate objects. The proposed method performs much better than existing methods for both binary and grayscale images in the sub-Nyquist condition, which is also robust to environment noise turbulence. Benefiting from advantages of ghost imaging, it may find applications for target recognition in the fields of remote sensing, military defense, and so on.

Spiral phase contrast imaging is an effective technique for the edge enhancement of a phase object. The spiral phase filter is the core component of the system and it provides sensitivity to the phase and amplitude gradients of the object. General spiral phase contrast imaging depends on the 4f imaging system in a single light beam with the coherent light source of visible or infrared wavelengths. Here, we constructed a non-local edge enhanced imaging system using an incoherent thermal light source. The detected object and the adopted spiral phase filter were non-locally placed in two separated light beams. The edge enhanced ghost image of the phase object can then be achieved through second-order intensity correlation measurements. The classical nature of the spatial degree of freedom of our proposed edge enhanced ghost imaging system was also demonstrated through the measurement of Bell-type inequality.

Ghost imaging (GI) is an unconventional optical imaging method making use of the correlation measurement between a test beam and a reference beam. GI using deep learning (GIDL) has earned increasing attention, as it can reconstruct images of high quality more effectively than traditional GI methods. It has been demonstrated that GIDL can be trained completely with simulation data, which makes it even more practical. However, most GIDLs proposed so far appear to have limited performance for random noise distributed patterns. This is because traditional GIDLs are sensitive to the under-estimation error but robust to the over-estimation error. An asymmetric learning framework is proposed here to tackle the unbalanced sensitivity to estimation errors of GIDL. The experimental results show that it can achieve much better reconstructed images than GIDL with a symmetric loss function, and the structural similarity index of GI is quadrupled for randomly selected objects.