The identification of hazardous chemicals based on hyperspectral imaging is an important emergent means for the prevention of explosion accidents and the early warning of secondary hazards. In this study, we used a combination of spectral curve matching based on full-waveform characteristics and spectral matching based on spectral characteristics to identify the hazardous chemicals, and proposed a method to quantitatively characterize the matching degree of the spectral curves of hazardous chemicals. The results showed that the four hazardous chemicals, sulfur, red phosphorus, potassium permanganate, and corn starch had bright colors, distinct spectral curve characteristics, and obvious changes in reflectivity, which were easy to identify. Moreover, the matching degree of their spectral curves was positively correlated with their reflectivity. However, the spectral characteristics of carbon powder, strontium nitrate, wheat starch, and magnesium–aluminum alloy powder were not obvious, with no obvious characteristic peaks or trends of change in reflectivity. Except for the reflectivity and the matching degree of the carbon powder being maintained at a low level, the reflectivity of the remaining three samples was relatively close, so that it was difficult to identify with the spectral curves alone, and color information should be considered for further identification.

To solve the problem of camera imaging quality degradation caused by defocusing during on-orbit operation, we propose an adaptive thermal refocusing system for high-resolution space cameras—the system comprising active and passive thermal refocusing. Using a space camera with a Ritchey–Chretien optical system as an example, the secondary mirror assembly was determined to be a passive thermal refocusing system, the primary mirror assembly being an active thermal refocusing system. We analyzed the system through structure/thermal/optics performance simulation when temperature variation Δ T was 5°C, 10°C, and 15°C; thermal vacuum experiments verified that the axial displacement of the active system was 0.0032, 0.0061, and 0.0090 mm, and the passive system was 0.00015, 0.00030, and 0.00069 mm, respectively. The data demonstrated the adaptive refocusing system theory to be consistent with the simulations and experiment, exhibiting high stability and reliability.

The multi-frame super-resolution techniques have been prosperous over the past two decades. However, little attention has been paid to the combination of deep learning and multi-frame super-resolution. One reason is that most deep learning-based super-resolution methods cannot handle variant numbers of input frames. Another reason is that it is hard to capture accurate temporal and spatial information because of the misalignment of input images. To solve these problems, we propose an optical-flow-based multi-frame super-resolution framework, which is capable of dealing with various numbers of input frames. This framework enables to make full use of the input frames, allowing it to obtain better performance. In addition, we use a spatial subpixel alignment module for more accurate subpixel-wise spatial alignment and introduce a dual weighting module to generate weights for temporal fusion. Both two modules lead to more effective and accurate temporal fusion. We compare our method with other state-of-the-art methods and conduct ablation studies on our method. The results of qualitative and quantitative analyses show that our method achieves state-of-the-art performances, demonstrating the advantage of the designed framework and the necessity of proposed modules.

Suppressing broadband stray light remains a persistent obstacle in advanced optical instrumentation, where conventional blackening treatments often fall short in spectral range, surface conformity, or substrate compatibility. Here we report a fabrication approach that directly forms parabolic‐shaped microstructures on a blackout‐ink coating through self‐assembled microsphere mask etching. The process is governed by a time‐dependent shadowing evolution around the microspheres, which enables continuous tuning of the sidewall curvature—a structural parameter that is difficult to access using conventional micro/nanofabrication and emerges as a key determinant of optical attenuation. The method requires only spray‐coating of the ink followed by dry etching and applies to the surfaces of glass, metal, polymer, and other engineering materials. Uniform microstructure formation is maintained across planar and curved surfaces without lithography or substrate‐dependent optimization. The parabolic‐shaped structures yield an average reflectance 0.89% over 300–1700 nm, enabled by continuous refractive‐index grading and efficient photon trapping associated with the controlled sidewall profile. This work establishes a practical route for producing broadband ultra‐black surfaces on real optical components while revealing a previously unrecognized mechanism linking microsphere‐mediated morphological evolution to macroscopic optical suppression. The approach offers a straightforward and broadly applicable pathway for improving stray‐light management in precision metrology, imaging, and spaceborne systems.

In the field of edge computing, quantizing convolutional neural networks (CNNs) using extremely low bit widths can significantly alleviate the associated storage and computational burdens in embedded hardware, thereby improving computational efficiency. However, such quantization also presents a challenge related to substantial decreases in detection accuracy. This paper proposes an innovative method, called Adaptive Global Power-of-Two Ternary Quantization Based on Unfixed Boundary Thresholds (APTQ). APTQ achieves adaptive quantization by quantizing each filter into two binary subfilters represented as power-of-two values, thereby addressing the accuracy degradation caused by a lack of expression ability of low-bit-width weight values and the contradiction between fixed quantization boundaries and the uneven actual weight distribution. It effectively reduces the accuracy loss while at the same time presenting strong hardware-friendly characteristics because of the power-of-two quantization. This paper extends the APTQ algorithm to propose the APQ quantization algorithm, which can adapt to arbitrary quantization bit widths. Furthermore, this paper designs dedicated edge deployment convolutional computation modules for the obtained quantized models. Through quantization comparison experiments with multiple commonly used CNN models utilized on the CIFAR10, CIFAR100, and Mini-ImageNet data sets, it is verified that the APTQ and APQ algorithms possess better accuracy performance than most state-of-the-art quantization algorithms and can achieve results with very low accuracy loss in certain CNNs (e.g., the accuracy loss of the APTQ ternary ResNet-56 model on CIFAR10 is 0.13%). The dedicated convolutional computation modules enable the corresponding quantized models to occupy fewer on-chip hardware resources in edge chips, thereby effectively improving computational efficiency. This adaptive CNN quantization method, combined with the power-of-two quantization results, strikes a balance between the quantization accuracy performance and deployment efficiency in embedded hardware. As such, valuable insights for the industrial edge computing domain can be gained.