As a widely used semiconductor material, silicon has been extensively used in many areas, such as photodiode, photodetector, and photovoltaic devices. However, the high surface reflectance and large bandgap of traditional bulk silicon restrict the full use of the spectrum. To solve this problem, many methods have been developed. Among them, the surface nanostructured silicon, namely black silicon, is the most efficient and widely used. Due to its high absorption in the wide range from UV-visible to infrared, black silicon is very attractive for using as sensitive layer of photodiodes, photodetector, solar cells, field emission, luminescence, and other photoelectric devices. Intensive study has been performed to understand the enhanced absorption of black silicon as well as the response extended to infrared spectrum range. In this paper, the application of black silicon is systematically reviewed. The limitations and challenges of black silicon material are also discussed. This article will provide a meaningful introduction to black silicon and its unique properties.

In order to solve the problem of near-infrared (NIR) absorbance attenuation of silicon, a method of preparing gold nanoparticles (AuNPs) on the micro–nano-structured black silicon (B-Si) is proposed. In this study, the local surface plasmon resonance (LSPR) of AuNPs excited by a light field is used to achieve B-Si materials with broad spectrum and high absorption. The results show that nanometer B-Si composited with 25-nm AuNPs has an average absorption of 98.6% in the spectral range of 400–1100 nm and 97.8% in the spectral range of 1100–2500 nm. Compared with ordinary B-Si, the absorption spectrum is broadened from 400–1100 nm to 400–2500 nm, and the absorption is increased from 90.1 to 97.8% at 1100–2500 nm. It is possible to use the B-Si materials in the field of NIR-enhanced photoelectric detection and micro-optical night vision imaging due to the low cost, high compatibility, and reliability.

To deal with low recognition accuracy and large time-consumption for dim, small targets in a smart micro-light sight, we propose a lightweight model DS_YOLO (dim and small target detection). We introduce the adaptive channel convolution module (ACConv) to reduce computational redundancy while maximizing the utilization of channel features. To address the misalignment problem in multi-task learning, we also design a lightweight dynamic task alignment detection head (LTD_Head), which utilizes GroupNorm to improve the performance of detection head localization and classification, and shares convolutions to make the model lightweight. Additionally, to improve the network’s capacity to detect small-scale targets while maintaining its generalization to multi-scale target detection, we extract high-resolution feature map information to establish a new detection head. Ultimately, the incorporation of the attention pyramid pooling layer (SPPFLska) enhances the model’s regression accuracy. We conduct an evaluation of the proposed algorithm DS_YOLO on four distinct datasets: CityPersons, WiderPerson, DOTA, and TinyPerson, achieving a 66.6% mAP on the CityPersons dataset, a 4.3% improvement over the original model. Meanwhile, our model reduces the parameter count by 33.3% compared to the baseline model.

The automatic registration of infrared and visible images in vehicular imaging systems remains challenging in vision-assisted driving systems because of differences in imaging mechanisms. Existing registration methods often fail to accurately register infrared and visible images in vehicular imaging systems due to numerous spurious points during feature extraction, unstable feature descriptions, and low feature matching efficiency. To address these issues, a robust and efficient registration of infrared and visible images for vehicular imaging systems is proposed. In the feature extraction stage, we propose a structural similarity point extractor (SSPE) that extracts feature points using the structural similarity between weighted phase congruency (PC) maps and gradient magnitude (GM) maps. This approach effectively suppresses invalid feature points while ensuring the extraction of stable and reliable ones. In the feature description stage, we design a rotation-invariant feature descriptor (RIFD) that comprehensively describes the attributes of feature points, thereby enhancing their discriminative power. In the feature matching stage, we propose an effective coarse-to-fine matching strategy (EC2F) that improves the matching efficiency through nearest neighbor matching and threshold-based fast sample consensus (FSC), while improving registration accuracy through coordinate-based iterative optimization. Registration experiments on public datasets and a self-established dataset demonstrate the superior performance of our proposed method, and also confirm its effectiveness in real vehicular environments.

Infrared images, rich in temperature information, have a broad range of applications. However, limitations in infrared imaging mechanisms and the manufacturing processes typically prevent uncooled infrared detector arrays from exceeding a resolution of one megapixel. Consequently, designing an efficient infrared image Super-Resolution (SR) algorithm is of significant importance. In this paper, we draw inspiration from the readout circuit structure prior to Uncooled Infrared Focal Plane Arrays (IRFPA), which scans infrared signals row by row and reads them out column by column. We propose an efficient Row-Column Transformer Block (RCTB) that splits features into rows and columns to effectively capture the spatio-temporal correlation between row-column pixels. Acknowledging the continuity of temperature information within the image and the correlation between adjacent pixel regions, we develop a Compact Convolution Block (CCB) that incorporates a U-shape Spatial Channel Attention Block (USCAB) to extract local features before the RCTB. During the training phase, we introduce a regularization control term into the loss function to enhance the reconstruction details of the infrared image. Building on these components, we propose a hybrid network named Efficient Infrared Image Super-Resolution (EIRSR), which achieves an excellent balance between performance and efficiency in terms of parameters and computational costs. Furthermore, we scale the model to create a family of variants, collectively known as EIRSRs. Extensive experiments demonstrate that EIRSR delivers competitive performance while maintaining a compact structure compared to superior methods.