Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.

In the production process of laser welding products, visual inspection is usually employed to rec- ognize welding spot locations and diagnose their quality faults. However, commonly used algorithms fail to succeed in both reliability and computational efficiency, especially when applied to assembly line. In this paper, a method based on deep learning algorithm and traditional computer vision (TCV) algorithm is proposed which achieves quality inspection of laser welding spots in the process of battery production. First, Compressed U-shape network (CU-net) is proposed to extract welding pads and welding spots. Then, a template-based method is proposed to confirm the validity of each welding spot. Finally, TCV heuristic algorithms are proposed to achieve three error detections, i.e, welding pad placed obliquely, electrode tab placed over highly, welding spot welded through. Moreover, we build a Welding Spot Quality Inspection Dataset taken from real assembly line. Compared with other pipelines including U- net, MaskRCNN and PSPNet, CU-net shows a significant su- periority in both processing speed and detection accuracy. The results of template-based method and TCV heuristic algorithms have shown the high computational efficiency and ensured the inspection accuracy. The inference time of whole method is less than 100ms with the implementation on NVIDIA 1060 and INTEL I7-6700.

Low‐light images suffer from severe noise and low illumination. In this work, the authors propose an adaptive low‐light raw image enhancement network to avoid parameter‐handcrafting in current deep learning models and to improve image quality. The proposed method can be divided into two sub‐models: brightness prediction and exposure shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time . The latter learns to approximate an exposure‐shifting operator ES, converting a low‐light image with real exposure time to a noise‐free image with guideline exposure time . Additionally, structural similarity loss and image enhancement vector are introduced to promote image quality, and a new campus image data set (CID) for training the proposed model is proposed to overcome the limitations of the existing data sets. In quantitative tests, it is shown that the proposed method has the lowest noise level estimation score compared with the state‐of‐the‐art low‐light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed.

Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters.

Deep learning algorithms have demonstrated significant potential in designing photonic-integrated devices, yet challenges persist in achieving high-precision predictions across wide wavelength ranges and multiple incident angles. Here, we propose a neural network-based method for rapid, high-accuracy design of multi-category grating couplers. The proposed architecture integrates the Kolmogorov-Arnold Network (KAN) module with a Transformer structure, enabling prediction of discrete grating coupler coupling efficiencies over incident angles ranging from 0$^{\circ }$ to 25$^{\circ }$ (5$^{\circ }$ increments) and wavelengths spanning 1300–1700nm. Post-training prediction accuracy exceeds 99.5%. By coupling this framework with inverse design algorithms, we achieve functional requirement-specific grating coupler designs within approximately 5 minutes. Experimental validation confirms the feasibility of method. Notably, the approach exhibits exceptional scalability, requiring minimal adjustments for application to arbitrary photonic device designs.