X-rays are widely used in probing inside information nondestructively, enabling broad applications in the medical radiography and electronic industries. X-ray imaging based on emerging lead halide perovskite scintillators has received extensive attention recently. However, the strong self-absorption, relatively low light yield and lead toxicity of these perovskites restrict their practical applications. Here, we report a series of nontoxic double-perovskite scintillators of Cs₂Ag_0.6Na_0.4In_1-yBi_yCl₆. By controlling the content of the heavy atom Bi³⁺, the X-ray absorption coefficient, radiative emission efficiency, light yield and light decay were manipulated to maximise the scintillator performance. A light yield of up to 39,000 ± 7000 photons/MeV for Cs₂Ag_0.6Na_0.4In_0.85Bi_0.15Cl₆ was obtained, which is much higher than that for the previously reported lead halide perovskite colloidal CsPbBr₃ (21,000 photons/MeV). The large Stokes shift between the radioluminescence (RL) and absorption spectra benefiting from self-trapped excitons (STEs) led to a negligible self-absorption effect. Given the high light output and fast light decay of this scintillator, static X-ray imaging was attained under an extremely low dose of ∼1 μGy_air, and dynamic X-ray imaging of finger bending without a ghosting effect was demonstrated under a low-dose rate of 47.2 μGy_air s⁻¹. After thermal treatment at 85 °C for 50 h followed by X-ray irradiation for 50 h in ambient air, the scintillator performance in terms of the RL intensity and X-ray image quality remained almost unchanged. Our results shed light on exploring highly competitive scintillators beyond the scope of lead halide perovskites, not only for avoiding toxicity but also for better performance.

Light flux from LED must be redistributed to meet the needs of lighting in most cases, a new method is proposed for its secondary optic design. Based on refractive equation and energy conservation, a set of first-order partial differential equations which represent the characters of LED source and desired illumination were presented. The freeform lens was constructed by solving these equations numerically. The numerical results showed that we can get a freeform lens for the illumination of uniformity near to 90%, with considerable high computation speed. This method can shorten the designing time of the freeform lens with high accepted tolerance.

The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational spectral imaging methods have resorted to computation power for reduced system volume, but still endure long computation time for iterative spectral reconstructions. Recently, deep learning techniques are introduced into computational spectral imaging, witnessing fast reconstruction speed, great reconstruction quality, and the potential to drastically reduce the system volume. In this article, we review state-of-the-art deep-learning-empowered computational spectral imaging methods. They are further divided into amplitude-coded, phase-coded, and wavelength-coded methods, based on different light properties used for encoding. To boost future researches, we’ve also organized publicly available spectral datasets.

Super-resolution microscopy is typically not applicable to in situ imaging through a narrow channel due to the requirement for complex optics. Although multimode fibres (MMFs) have emerged as a potential platform for cost-effective and precise endoscopic imaging, they suffer from extreme sensitivity to bending and other external conditions. Here we demonstrate imaging through a single thin MMF for in vivo light-field encoded imaging with subcellular resolution. We refer to the technique as spatial-frequency tracking adaptive beacon light-field-encoded (STABLE) endoscopy. Spatial-frequency beacon tracking provides up to 1 kHz disorder tracking frequency, thus ensuring stable imaging through long-haul MMFs under fibre bending and various operating conditions. The full-vector modulation and fluorescence emission difference are combined to enhance the imaging signal-to-noise ratio and achieve a subdiffraction resolution of 250 nm. We integrate STABLE in a white-light endoscope and demonstrate cross-scale imaging in a bronchus model and in vivo imaging in mice models. The high-resolution and resilience to observation in a minimally invasive manner paves the way to the expansion of MMF in endoscopy to the study of disease mechanisms in biomedical sciences and clinical studies.

Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available, computational spectral cameras represent a compact, lightweight, and inexpensive solution. However, the tradeoff between spatial and spectral resolutions, dominated by the limited data volume and environmental noise, limits the potential of these cameras. In this study, we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular, using advanced artificial intelligence in filter design and spectrum reconstruction, we achieved 7000–11,000 times faster signal processing and ~10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view, previously unreachable with compact computational spectral cameras.