Zhixi Wang

As a next-generation automotive display technology, Augmented Reality Head-Up Display (AR-HUD) has demonstrated immense potential in reshaping driving safety and enhancing the human–computer interaction experience. To address the challenges of barrel distortion and perspective distortion inherent in HUD systems, we propose a joint-learning-based dual-path pre-correction method. This approach employs a shared encoder to extract image features, which are then decoupled into two parallel branches: a classification branch and a distortion flow prediction branch. Building upon this architecture, a model-fitting method is introduced to estimate the distortion model parameters in the parameter space using the predicted distortion types and flows, thereby reconstructing a refined distortion flow. Finally, image rectification is achieved through a resampling method. On the ARHDD dataset, the proposed method achieves a PSNR of 24.617 dB (barrel) and 25.062 dB (perspective), an SSIM of 0.845 and 0.873, and an NRMSE of 0.163 and 0.157, respectively. On the Places 365 dataset, it achieves a PSNR of 23.914 dB (barrel) and 21.870 dB (perspective), an SSIM of 0.812 and 0.748, and an NRMSE of 0.174 and 0.211, respectively. Both quantitative and qualitative comparative experiments against other state-of-the-art methods demonstrate that the proposed approach achieves superior correction performance for both types of distortion. Finally, the simulation verification of the HUD system proved that this correction method demonstrated excellent potential, but further verification is still needed in a real or semi-real environment.

Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with large parameters, high computational complexity and difficulty in deploying on edge devices, this paper proposes a lightweight glare suppression network (LGSNet) based on ghost depthwise separable convolution and Lightweight Parallel Attention. Based on the U-Net architecture, the network introduces ghost depthwise separable convolution blocks (GhostDSC) in the encoder and decoder, which generates ghost features through cheap linear transformations by exploiting feature map redundancy, significantly reducing model parameters and computational costs while maintaining feature representation ability. Meanwhile, a Lightweight Parallel Attention (LPA) module is designed in the decoder stage, which integrates channel attention and pixel attention in parallel, enhancing the network’s attention to glare regions and edge details with extremely low parameter increment to improve detail recovery accuracy. In addition, a joint loss function consisting of background loss, glare loss and reconstruction loss is constructed to collaboratively optimize glare suppression and detail preservation. Experimental results on the public Flare7K++ dataset and the self-built nighttime road glare dataset NRGD show that the proposed method has only 7.45 M parameters, much lower than standard U-Net and Uformer. It achieves competitive results on full-reference metrics such as PSNR, SSIM, LPIPS and no-reference metrics such as NIQE, BRISQUE, PIQE, and can effectively suppress various types of glare interference and restore obscured scene details. It achieves a superior trade-off between model complexity and enhancement performance, significantly reducing the parameter count and computational overhead compared to heavy baselines, thereby offering a highly efficient solution for resource-aware glare suppression tasks.

Considering the large-area distribution, smooth brightness gradients, and blurred boundaries of Mura defects in real industrial scenarios, as well as the challenge of balancing accuracy and efficiency in existing methods, we propose a lightweight deep learning-based detection method for large-area Mura defects, termed SIFNet. The SIFNet adopts a classical encoder-decoder architecture with MobileNet-V2 as the backbone. Furthermore, we design a Graph-based Semantic Interscale-fusion Block (GSIB) that integrates the Semantic Fluid Aggregation Module (SFAM) and the Semantic Graph Inference Module (SGIM) to collaboratively extract high-level semantic features across multiple scales and establish abstract semantic representations for accurately localizing large-area Mura defects. Specifically, SFAM leverages a global attention mechanism to extract cross-spatial semantic flows, guiding the model to focus on potential brightness anomaly regions in the image and SGIM explicitly models the semantic relationships between multi-scale features using graph convolution, enhancing the model's ability to interpret regions with blurred boundaries and ambiguous structures. To further improve the model’s sensitivity to edges in regions with smooth brightness transitions, we introduce a NeighborFusion Edge Enhancement Module (NEEM). This module integrates depthwise separable convolutions with a spatial attention mechanism and introduces a CrossNorm-based feature alignment strategy to enhance spatial collaboration across feature layers. Additionally, an edge enhancement mechanism is employed to significantly improve the model’s ability to delineate blurred Mura defect boundaries, while keeping computational cost low and strengthening edge perception and representation. Extensive quantitative and qualitative experiments on three large-area Mura defect datasets constructed in this study demonstrate that SIFNet achieves excellent detection performance with only 3.92M parameters and 6.89 GFLOPs, striking an effective balance between accuracy and efficiency, and fully meeting the demands of industrial deployment.

Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions and recover fine details. To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. To advance the research in nighttime glare suppression, we constructed and publicly released the Night Road Glare Dataset (NRGD) captured in real nighttime road scenarios, enriching the evaluation system for this task. Experiments conducted on the Flare7K++ and NRGD, using five evaluation metrics and comparing six state-of-the-art methods, demonstrate that our method achieves superior performance in both subjective and objective metrics compared to existing advanced methods.

Aiming at the problems of difficult construction and occlusion of the initial structure of an automotive augmented reality head-up display (AR-HUD), a method to quickly build the initial structure of an automotive AR-HUD is proposed. Firstly, the position of the mirrors in the initial structure is calculated based on the Rodrigues rotation formula. Secondly, the position of the mirrors is restricted by constraints during optimization to prevent the problem of structural occlusion. Finally, a virtual image display system with a visual distance of 7.5 m and a field of view angle of 10° × 5° is designed. The image quality analysis of the optimized system shows that the light spots in each field of view are all within Airy spots. At the spatial cutoff frequency of the virtual image plane of the optical system, the modulation transfer function (MTF) value of the full field of view is basically greater than 0.5, and the distortion is less than 1%. Finally, using the image for simulation, the results of the simulation image are satisfying, which proves the validity and feasibility of the structural design. It provides a useful reference for the structural design of the remote head-up display system.

To address the issue of low detection accuracy caused by low contrast in color Mura defects, this paper proposes a color Mura defect detection method based on channel contrast sensitivity function (CSF) filtering. The RGB image of the captured liquid crystal display (LCD) display is converted to the Lab color space, and the Weber contrast feature maps of the Lab channel images are calculated. Frequency domain filtering is performed using the CSF to obtain visually sensitive Lab feature maps. Color Mura defect detection is achieved by employing adaptive segmentation thresholds based on the fused feature maps of the L channel and ab channels. The color Mura evaluation criterion is utilized to quantitatively assess the defect detection results. Experimental results demonstrate that the proposed method achieves an accuracy rate of 87% in color Mura defect detection, outperforming existing mainstream detection methods.

Display color point defect detection is an important link in the display quality inspection process. To improve the detection accuracy of color point defects, a color point defect detection method based on color salient features is proposed. Color point defects that conform to the perception of the human vision are used as the key point for detection. First, the human visual perception constraint coefficient is used to correct the RGB three-channel image to obtain the color-channel-transformed image. Then, the local contrast method is used to extract the point features of the color channel, which achieves point defect enhancement, noise and background suppression. Finally, the mean and standard deviation of the defect feature maps of R, G, and B channels are calculated. The maximum mean and standard deviation are selected as thresholds using the maximum fusion criterion to perform binarization segmentation of the defect feature maps of R, G, and B channels. An OR operation was performed on the segmented images and the point defect segmentation results were combined. The experimental results show that the average detection accuracy and recall of the algorithm is higher than 94%, which is a significant improvement compared with mainstream detection methods and meets the needs of industrial production.

Display color line defect detection is an important step in the production quality inspection process. In order to improve the detection accuracy of low contrast line defects, we propose a display line defect detection method based on color feature fusion. The color saliency channels in the RG|GR and BY|YB channels were obtained using the relative entropy maximum criterion. Then, RG|GR were combined with the a channel and BY|YB with the b channel to calculate the red-green and the blue-yellow color fusion maps. The fusion color saliency map of the red-green and the blue-yellow color fusion maps was obtained by color feature fusion. Finally, the segmentation threshold was calculated according to the mean and standard deviation of the fusion color saliency map. The fused color saliency map was binarized and segmented to obtain a binary map of color line defects. The experimental results show that for the detection of multi-background offline defects, the detection accuracy of the algorithm in this paper is better than 90%, while other mainstreams fail to detect. Compared with state-of-the-art saliency detection algorithms, our method is capable of real-time low-contrast line defect detection.

Display crosstalk defect detection is an important link in the display quality inspection process. We propose a crosstalk defect detection method based on salient color channel frequency domain filtering. Firstly, the salient color channel in RGBY is selected by the maximum relative entropy criterion, and the color quaternion matrix of the displayed image is formed with the Lab color space. Secondly, the image color quaternion matrix is converted into the logarithmic spectrum in the frequency domain through the hyper-complex Fourier transform. Finally, Gaussian threshold band-pass filtering and hyper-complex inverse Fourier transform are used to separate the low-contrast defects and background of the display image. The experimental results show that the accuracy of the proposed algorithm reaches 96% for a variety of crosstalk defect detection. Compared with the current advanced defect detection algorithms, the effectiveness of the proposed method for low-contrast crosstalk defect detection is confirmed.

Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, and complex imaging conditions, mainly for two reasons: (1) accurate location of salient objects; and (2) subtle boundaries of salient objects. This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI. Specifically, the proposed semantic guided decoder (SGD) roughly but accurately locates the multi-scale object by aggregating multiple high-level features, and then this global semantic information guides the integration of subsequent features in a step-by-step feedback manner to make full use of deep multi-level features. Simultaneously, the proposed parallel attention fusion (PAF) module combines cross-level features and semantic-guided information to refine the object’s boundary and highlight the entire object area gradually. Finally, the proposed network architecture is trained through an end-to-end fully supervised model. Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that our SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.