Laser fabrication of metallic superhydrophobic surfaces (SHSs) for anti-frosting has recently attracted considerable attention. Effective anti-frosting SHSs require the efficient removal of condensed microdroplets through self-propelled droplet jumping, which is strongly influenced by the surface morphology. However, detailed analyses of the condensate self-removal capability of laser-structured surfaces are limited, and guidelines for laser processing parameter control for fabricating rationally structured SHSs for anti-frosting have not yet been established. Herein, a series of nanostructured copper-zinc alloy SHSs are facilely constructed through ultrafast laser processing. The surface morphology can be properly tuned by adjusting the laser processing parameters. The relationship between the surface morphologies and condensate self-removal capability is investigated, and a guideline for laser processing parameterization for fabricating optimal anti-frosting SHSs is established. After 120 min of the frosting test, the optimized surface exhibits less than 70% frost coverage because the remarkably enhanced condensate self-removal capability reduces the water accumulation amount and frost propagation speed (<1 μm/s). Additionally, the material adaptability of the proposed technique is validated by extending this methodology to other metals and metal alloys. This study provides valuable and instructive insights into the design and optimization of metallic anti-frosting SHSs by ultrafast laser processing.

Hyperspectral (HS) imaging has achieved breakthroughs in many applications, such as remote sensing and object recognition. However, the spatial resolution of HS images is still insufficient due to the limitations of sensor technology and cost. In this article, we propose an HS image superresolution method that combines low-resolution (LR) HS images and high-resolution (HR) panchromatic (PAN) images. To exploit the spectral signatures in the LR-HS images while introducing details from the HR-PAN images during the image fusion procedure, an image matting model is used to fuse the original LR-HS images and the HR-PAN images. Specifically, to preserve the spectral components during the fusion procedure, two different alpha channels in the image matting model are generated based on the HS and PAN image structure tensors, which suppress spectral distortion and improve the quality of the reconstructed HR-HS image. Experimental results based on public datasets demonstrate the advantage of our proposed method in both preserving spectral information and enhancing HS image spatial resolution.

Aiming to address the problem of moving mirror speed fluctuations in moving mirror control systems, an improved active disturbance rejection double closed-loop controller (IADR-DCLC) is proposed and verified by simulation to realize the high-performance control of a moving mirror control system. First, the mathematical model of a rotary-type voice coil motor (RT VCM) is established, and the relationship between the angular velocity of the RT VCM and the optical path scanning velocity is analyzed. Second, in order to suppress the model uncertainty and external disturbance of the system, an improved active disturbance rejection controller (IADRC) is proposed. Compared with a conventional ADRC, the tracking differentiator of the proposed IADRC is replaced with desired signal optimization (DSO), and the actual speed is introduced to the extended state observer (ESO). The IADRC is used in the position–speed double closed-loop control model. Finally, the simulation results show that the IADR-DCLC has not only a good tracking effect but also a good anti-interference ability and can meet the requirements of the moving mirror control system for the uniformity of optical-path scanning speed and accurate control of the position of the moving mirror.

Si₃N₄ ceramics with a microscale rice leaf structure (MRLS) and titanium alloy were connected via brazing, and the influence of the surface microstructure on the ceramic connection was analyzed. MRLS fabrication is an efficient and high-degree-of-freedom method that can be used to change a material’s surface morphology and wettability. The MRLS was obtained at a laser power of 110 W, with line spacings of 100 and 50 μm. The laser-treated surface included nanoparticles and micro particles, exhibiting a coral-like structure after agglomeration. When the MRLS was used to braze the titanium alloy, no defects were observed at the brazing interface, and the formation was excellent. Throughout the brazed joint, the MRLS remained intact and formed a strong metallurgical bond with the brazing filler metal. A finite element analysis was performed to study the cross-sectional morphology after joint fracture; from the load-time curve, it was found that the MRLS on the surface not only helped improve the mechanical occlusion and brazing area at the interface, but also helped generate compressive stress on the Si₃N₄ side. Crack propagation was hindered, thereby increasing the joint strength.

An iterative image restoration algorithm, directed at the image deblurring problem and based on the concept of long- and short-exposure deblurring, was proposed under the image deconvolution framework by investigating the imaging principle and existing algorithms, thus realizing the restoration of degraded images. The effective priori side information provided by the short-exposure image was utilized to improve the accuracy of kernel estimation, and then increased the effect of image restoration. For the kernel estimation, a priori filtering non-dimensional Gaussianity measure (BID-PFNGM) regularization term was raised, and the fidelity term was corrected using short-exposure image information, thus improving the kernel estimation accuracy. For the image restoration, a P norm-constrained relative gradient regularization term constraint model was put forward, and the restoration result realizing both image edge preservation and texture restoration effects was acquired through the further processing of the model results. The experimental results prove that, in comparison with other algorithms, the proposed algorithm has a better restoration effect.

Under night vision conditions, visible-spectrum images often fail to capture background details. Conventional visible and infrared fusion methods generally overlay thermal signatures without preserving latent features in low-visibility regions. This paper proposes a novel deep learning-based fusion algorithm to enhance visual perception in night driving scenarios. Firstly, a local adaptive enhancement algorithm corrects underexposed and overexposed regions in visible images, thereby preventing oversaturation during brightness adjustment. Secondly, ResNet152 extracts hierarchical feature maps from enhanced visible and infrared inputs. Max pooling and average pooling operations preserve critical features and distinct information across these feature maps. Finally, Linear Discriminant Analysis (LDA) reduces dimensionality and decorrelates features. We reconstruct the fused image by the weighted integration of the source images. The experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods in both objective metrics and subjective visual assessments.