We propose a hybrid computational near-eye light field display based on micro-lens arrays (MLAs) and structure parameters prioritization method (SPPM) that can render correct accommodation and expand the depth of field (DOF). The proposed display consists of two MLAs and two micro-displays. Two sets of depth-sliced images are superimposed to reconstruct the object's light field. The system's ray resolution and the spectra difference between the proposed light field and the conventional one are discussed. The DOF of the proposed system is extended without the requiring of high refresh rate display. Furthermore, SPPM is proposed to extend the spatial frequency of the light field. A prototype is demonstrated and the experimental result shows that the depth field of the reconstructed images is significantly increased compared with conventional near-eye light field display based on single MLA.

With the intensification of global climate change, extreme precipitation events are occurring more frequently, making the monitoring and management of urban flooding a critical global issue. Urban surveillance camera sensor networks, characterized by their large-scale deployment, rapid data transmission, and low cost, have emerged as a key complement to traditional remote sensing techniques. These networks offer new opportunities for high-spatiotemporal-resolution urban flood monitoring, enabling real-time, localized observations that satellite and aerial systems may not capture. However, in low-light environments—such as during nighttime or heavy rainfall—the image features of flooded areas become more complex and variable, posing significant challenges for accurate flood detection and timely warnings. To address these challenges, this study develops an imaging model tailored to flooded areas under low-light conditions and proposes an invariant feature extraction model for flooding areas within surveillance videos. By using extracted image features (i.e., brightness and invariant features of flooded areas) as inputs, a deep learning-based flood segmentation model is built on the U-Net architecture. A new low-light surveillance flood image dataset, named UWs, is constructed for training and testing the model. The experimental results demonstrate the efficacy of the proposed method, achieving an mRecall of 0.88, an mF1_score of 0.91, and an mIoU score of 0.85. These results significantly outperform the comparison algorithms, including LRASPP, DeepLabv3+ with MobileNet and ResNet backbones, and the classic DeepLabv3+, with improvements of 4.9%, 3.0%, and 4.4% in mRecall, mF1_score, and mIoU, respectively, compared to Res-UNet. Additionally, the method maintains its strong performance in real-world tests, and it is also effective for daytime flood monitoring, showcasing its robustness for all-weather applications. The findings of this study provide solid support for the development of an all-weather urban surveillance camera flood monitoring network, with significant practical value for enhancing urban emergency management and disaster reduction efforts.

This paper first presents a switchable photonic nanojet (PNJ) generated by a polystyrene (PS) microsphere immersed in nematic liquid crystals (NLCs). The PNJ is switched by applying external voltage, which originates from the refractive index change in the surrounding medium caused by the field-induced realignment of liquid crystal molecules. By tuning the refractive index of NLCs larger or smaller than that of the PS microsphere, the PNJ can be switched off or on. Moreover, we present an optimization study to seek a better electric energy focusing property of the PNJ. Our results reveal that the switchability of PNJ can be optimized by applying a shorter incident wavelength, a double-layer microsphere, and a PS ellipsoid. The full width at half-maximum (FWHM) generated by the PS ellipsoid is narrower than that generated by the microsphere with a shorter incident wavelength. The intensity contrast of the PS ellipsoid is higher than that of the double-layer microsphere. As a whole, the switchability of PNJ can be best optimized by a PS ellipsoid. This should open the way for the development of integrated photonic devices.

In this paper, high images quality switchable 2D/3D display is achieved by carefully matching the high-resolution LCD pixel structure and liquid crystal Graded Index (GRIN) lens. A parallelogram LCD pixel structure is proposed, in which the edges of each sub-pixels are slanted. At the top of the LCD pixels, the liquid crystal GRIN lens is also tilted to reduce the moiré effect and the tilt angle is carefully aligned with the edges of sub-pixels of LCD. This accurate alignment is useful to eliminate the crosstalk and improves the imaging quality of 3D display. 2D/3D switchable display is realized by tuning the liquid crystal in the GRIN lens. Based on this, a 3D display based on 32-inch 8K4K resolution LCD display and Liquid crystal GRIN lens was designed.

In order to address the vergence-accommodation conflict problem, a generalized model consisting of a human visual system for the 4D light field display system was proposed. This model includes the key factors, such as retinal resolution, the central depth plane (CDP), and the proposed spatial loss factor, for the light field display system. The spatial resolution of the target plane in the depth of field (DOF) were quantitatively evaluated based on the proposed model. The results showed that the inconsistency of spatial resolution in DOF results in unstable eye accommodation response. Based on the fovea resolution-limit, we evaluated and simulated the resolution of perceived images on retina and the accommodation response based on spatial loss factor. The simulation results verified that a near-eye light field display (NE-LFD) configuration with a spatial loss factor greater than 0.8, corresponding to 2 by 2 views, or a minimum spatial loss factor 0.6, corresponding to 3 by 3 views, has the ability to render a nearly correct focus cues and accommodation response.