Yiting Yu

As a type of inverse‐designed micro/nanophotonic device, planar super‐oscillation diffractive lenses (PSODLs) require complex trade‐offs among multiple key performance metrics to optimize their performance, including spot size, focusing efficiency, field of view (FOV), depth of focus (DOF), and working bandwidth. This review focuses on two representative types of PSODLs, super‐oscillatory lenses (SOLs) and supercritical lenses (SCLs). Both can generate far‐field focal spots with sizes below the diffraction limit, but they exhibit distinct optical‐field focusing characteristics, and there are trade‐offs among performance parameters. Therefore, this review provides specific guidance for the selection and optimization of PSODLs in accordance with application scenarios. To better promote the practical application of these kinds of lenses, a systematic analysis of typical applications in areas such as super‐resolution imaging, micro/nanofabrication, and high‐precision measurement is provided. Potential extensions of PSODLs to integrated optical imaging systems, optical sensing, particle manipulation, and optical storage are also discussed. Future practical deployment of PSODLs will rely on application‐oriented multiobjective optimization, robust and scalable fabrication, active system integration, and standardized performance evaluation.

Dynamic color modulation holds great promise for next-generation display technologies, offering wide applicability in smart displays, adaptive surfaces, and wearable electronics. However, conventional WO₃-based electrochromic (EC) devices face inherent limitations, making it difficult to simultaneously achieve broad color tunability and rapid response performance. Here, we proposed an inorganic all-solid-state composite structure that synergistically integrates structural color and WO₃-based EC modulation via color superposition. The underlying Ag/Al₂O₃/Ni/Al₂O₃ stack provides static structural coloration, while the top ITO/WO₃/LiTaO₃/NiO/ITO stack enables dynamic, voltage-controlled color tuning. The resulting composite structure achieves precise, reversible color modulation under 1.5 V, with colored and bleached times of 2.5 s and 1 s, respectively, and excellent stability over 1000 cycles. After applying the voltage to induce coloration, the device can maintain its colored state without a continuous power supply, demonstrating the intrinsic non-volatile EC behavior. This integrated approach effectively overcomes the narrow color range and sluggish switching of conventional WO₃-based EC systems, providing a simple-to-fabricate, versatile, and energy-efficient platform for high-performance adaptive optical devices. Looking ahead, extending the color tunability of EC layers or introducing multiple EC modulation layers could further expand the dynamic color range, paving the way for multicolor, reconfigurable reflective displays and smart optical systems.

Polarization-based laser vision imaging offers high accuracy, non-contact sensing, and strong robustness to interference, making it promising for weld seam localization and intelligent welding. However, for planar fillet-weld workpieces with highly reflective metallic surfaces, reliable laser stripe segmentation and precise extraction of centerlines and feature points remain challenging. This is because inter-reflection interference and camera saturation severely degrade measurement accuracy. In this paper, we propose a novel physically-based polarization reflection separation method using polarization features and spatial structural information for robust laser stripe extraction. The physical principles and polarization properties of direct- and inter-reflections from metallic surfaces are investigated through theoretical modeling and simulation. By combining the Stokes parameter S₂ with a saturation region segmentation criterion, the inter-reflection regions of the laser stripe are automatically determined. An adaptive mask dilation approach is then introduced to suppress saturation-induced interference without complex parameter tuning. Finally, the centerlines and feature points are accurately extracted using the gray-gravity method with least-squares fitting. Experiments demonstrate superior segmentation and extraction performance under strong reflective interference, achieving an average feature-point extraction error of 2.36 pixels, a repeatability error within ±1.16 pixels, and an average computational time of 0.2531 s. Moreover, comprehensive evaluations across varying groove angles, laser rotation angles, exposure times, surface conditions and dynamic robotic platforms further validate the robustness and effectiveness of the proposed method. This study highlights the potential of polarization imaging for addressing optical interference challenges in industrial welding scenarios, providing a solid foundation for intelligent and automated welding.

Longwave infrared (LWIR) spectral imaging offers unique capabilities for gas/liquid detection, mineral exploration, environmental monitoring, and military security applications. Conventional LWIR spectral imagers, however, are hampered by reliance on existing dispersion elements, resulting in inflexible operation, limited spectral channels, and excessive bulk, which preclude deployment in intelligent, multi-scenario settings. To address such an issue, we introduce a compact adaptive spectral imager (CASI) leveraging a LWIR large-aperture micro-electro-mechanical systems based Fabry-Perot filtering chip (MEMS-FPFC). A modular chip-front optical architecture yields an ultra-compact volume (150 × 77 × 88 mm³), low-weight (1.11 kg), and “plug-and-play” operation. A synergistic adaptation control system exploits the intrinsic programmability of the MEMS-FPFC to enable diverse adaptive imaging modes-including programmable spectral acquisition spanning global coarse/fine scans, localized fine scans, and arbitrary spectral band combinations-tailored to specific application demands. This adaptability, coupled with spectral channel selection and image fusion algorithms, facilitates efficient and precise target identification. The CASI’s compactness and programmability enable integration onto small unmanned platforms, advancing intelligent LWIR spectral imaging for field-deployable applications in industrial pollution monitoring, mineral prospecting, and security.

Long-wave infrared polarimetric imaging plays a crucial role in target detection, material classification, and scene understanding in complex environments. However, high-intensity background clutter significantly degrades the image quality captured by division-of-focal-plane (DoFP) polarimeters, thereby limiting the performance of subsequent target detection algorithms. Meanwhile, conventional post-processing methods passively suppress background clutter through image analysis alone, lacking in-depth exploration of the underlying hardware framework. As a result, their clutter suppression capability remains limited in complex scenes. In this paper, we propose Active Defocus Blurring Enhancement (ADBE), a simple yet effective approach that integrates both hardware and algorithm strategies to flexibly manipulate the imaging process. Specifically, we reveal how the lens defocus influences the measurement of polarization information and the suppression of background clutter. Building on this insight, an active defocus blurring strategy is developed, transforming a conventional imaging limitation into a powerful mechanism for optimizing imaging parameters based on scene conditions. The outdoor experimental results demonstrate that, compared with conventional in-focus approach, the proposed method can significantly suppress the background clutter and enhance the contrast between the target and background, particularly in degree of linear polarization (DoLP) images. These findings highlight the potential of ADBE for next-generation intelligent imaging systems that autonomously adapt to diverse application scenarios.

Longwave infrared (LWIR) spectral imaging offers unique capabilities for gas/liquid detection, mineral exploration, environmental monitoring, and military security applications. Conventional LWIR spectral imagers, however, are hampered by reliance on existing dispersion elements, resulting in inflexible operation, limited spectral channels, and excessive bulk, which preclude deployment in intelligent, multi-scenario settings. To address such issue, we introduce a compact adaptive spectral imager (CASI) leveraging a LWIR large-aperture micro-electro-mechanical systems based Fabry-Perot filtering chip (MEMS-FPFC). A modular chip-front optical architecture yields an ultra-compact volume (150×77×88 mm³), low-weight (1.11 kg), and “plug-and-play” operation. A synergistic adaptation control system exploits the intrinsic programmability of the MEMS-FPFC to enable diverse adaptive imaging modes-including programmable spectral acquisition spanning global coarse/fine scans, localized fine scans, and arbitrary spectral band combinations-tailored to specific application demands. This adaptability, coupled with spectral channel selection and image fusion algorithms, facilitates efficient and precise target identification. The CASI’s compactness and programmability enable integration onto small unmanned platforms, advancing intelligent LWIR spectral imaging for field-deployable applications in industrial pollution monitoring, mineral prospecting, and security.