Zi-Qing Zhao

Ultraviolet (UV) imaging can reveal structural flaws, fluorescent markers, and characteristic signals generated by material changes that are difficult to detect in visible-light imaging and has been applied in numerous fields. However, in traditional UV imaging, it is challenging to simultaneously acquire reflection/transmission and UV-excited fluorescence imaging, often requiring additional optical filters to eliminate interference bands. Here, we demonstrate a dual-mode UV lensless imaging method based on diffuser speckle modulation, which enables direct reconstruction of both reflection/transmission and fluorescence images from a single-shot measurement. Compared with conventional methods, our scheme eliminates the need for optical lenses and filters, enabling an ultra-compact lensless system. This approach can be further integrated with other image post-processing operations or general computer vision tasks for the development of task-oriented intelligent UV cameras.

Single-pixel imaging (SPI), especially when integrated with deep neural networks like deep image prior networks (DIP-Net) or data-driven networks (DD-Net), has gained considerable attention for its capability to generate high-quality reconstructed images, even in the presence of sub-sampling conditions. However, DIP-Net often requires thousands of iterations to achieve high-quality image reconstruction, and DD-Net performs optimally only when the target closely resembles the features present in its training set. To overcome these limitations, we propose a dual-network iterative optimization (SPI-DNIO) framework that combines the strengths of both DD-Net and DIP-Net. It has been demonstrated that this approach can recover high-quality images with fewer iteration steps. Furthermore, to address the challenge of SPI inputs having less effective information at low sampling rates, we have designed a residual block enriched with gradient information, which can convey details to deeper layers, thereby enhancing the deep networks learning capabilities. We have applied these techniques to both indoor experiments with active lighting and outdoor long-range experiments with passive lighting. Our experimental results confirm the exceptional reconstruction capabilities and generalization performance of the SPI-DNIO framework.

Single-pixel imaging (SPI) has gained considerable attention over the past two decades but is still inadequate for imaging moving objects in practical scenarios. Recently, spinning masks have been employed for real-time SPI due to their fast modulation speeds. However, the intensity fluctuations caused by ambient light can overlap with the desired modulation from the mask, leading to a significant degradation in image quality, making it unable to function properly in environments with unstable lighting. Here we propose a rotating disc encoded with cyclic S-matrices, leveraging both reflective and transmissive signals for differential detection. Simulations and experiments demonstrate interference-resistant real-time SPI, achieving 31 fps at about 60 kHz modulation rates. This approach effectively suppresses external noise and is extendable to infrared and terahertz bands, advancing SPI’s practical applications.