Zheng Zhang

Underwater laser range-gated imaging (ULRGI) effectively suppresses water backscattering via time-slicing mechanisms, making it a primary modality for underwater vision. However, factors such as the inherent optical properties of water, intra-slice residual scattering, gating timing errors, and sensor noise make it difficult to separate target signals from the background. Consequently, the resulting images are generally affected by texture degradation and low contrast, severely limiting the accuracy of downstream tasks like object detection and environmental perception. To this end, we propose the use of a Wavelet-guided Multi-stage Gated Enhancement Network (WMGNet). Operating progressively across three stages, WMGNet’s first two stages employ an encoder–decoder architecture that leverages multi-scale frequency decomposition in the wavelet domain to pinpoint intra-slice scattering and decouple target signals from noise. To precisely extract fine details, we design a TextureBlock integrating feature gating (ConvGLU) and high-frequency attention (HFAttention). Additionally, a pixel-wise ground-truth guided attention module (GGAM) is introduced to optimize the precision and target-specificity of multi-stage feature fusion. Extensive comparative and ablation experiments demonstrate that the proposed WMGNet effectively eliminates scattering interference and restores texture details in underwater imaging. On our custom ULRGI dataset, it achieves state-of-the-art performance with a PSNR of 36.31 dB, an SSIM of 0.921, an MAE of 2.672, and an LPIPS of 0.060. Notably, it outperforms the second-best method by a margin of 3.06 dB in PSNR and reduces the MAE by 50.69%. Furthermore, evaluations on three public datasets confirm its robust cross-scenario generalization, yielding competitive PSNR values of 33.22 dB, 31.59 dB, and 32.06 dB, respectively. Overall, WMGNet provides a highly effective and robust solution for high-resolution underwater imaging.

Underwater laser range-gated imaging (ULRGI) effectively suppresses backscatter from water bodies through a time-gated photon capture mechanism, significantly extending underwater detection ranges compared to conventional imaging techniques. However, as imaging distance increases, rapid laser power attenuation causes localized pixel loss in captured images. To address ULRGI’s limitations in multi-frame stacking—particularly poor real-time performance and artifact generation—this paper proposes the Photon-Level Physically Guided Underwater Laser-Gated Image Restoration Network (PLPGR-Net). To overcome image degradation caused by water scattering and address the challenge of strong coupling between target echo signals and scattering noise, we designed a three-branch architecture driven by photon-level physical priors. This architecture comprises: scattering background suppression module, sparse photon perception module, and enhanced U-Net high-frequency information recovery module. By establishing a multidimensional physical constraint loss system, we guide image reconstruction across three dimensions—pixels, features, and physical laws—ensuring the restored results align with underwater photon distribution characteristics. This approach significantly enhances operational efficiency in critical applications such as underwater infrastructure inspection and cultural relic detection. Comparative experiments using proprietary datasets and state-of-the-art denoising and underwater image restoration algorithms validate the method’s outstanding performance in deeply integrating physical interpretability with deep learning generalization capabilities.

Autonomous vehicle technology is advancing, with 3D object detection based on point clouds being crucial. However, point clouds’ irregularity, sparsity, and large data volume, coupled with irrelevant background points, hinder detection accuracy. We propose a two-stage multi-scale 3D object detection network. Firstly, considering that a large number of useless background points are usually generated by the ground during detection, we propose a new ground filtering algorithm to increase the proportion of foreground points and enhance the accuracy and efficiency of the two-stage detection. Secondly, given that different types of targets to be detected vary in size, and the use of a single-scale voxelization may result in excessive loss of detailed information, the voxels of different scales are introduced to extract relevant features of objects of different scales in the point clouds and integrate them into the second-stage detection. Lastly, a multi-scale feature fusion module is proposed, which simultaneously enhances and integrates features extracted from voxels of different scales. This module fully utilizes the valuable information present in the point cloud across various scales, ultimately leading to more precise 3D object detection. The experiment is conducted on the KITTI dataset and the nuScenes dataset. Compared with our baseline, “Pedestrian” detection improved by 3.37–2.72% and “Cyclist” detection by 3.79–1.32% across difficulty levels on KITTI, and was boosted by 2.4% in NDS and 3.6% in mAP on nuScenes.

With the increasing demand from unmanned driving and robotics, more attention has been paid to point-cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features regarding reflectivity of the point cloud to maximize the use of information in the point cloud. Secondly, we enhanced the distance features and raw features, which we collectively refer to as self-features of the key points, in set abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show that the enhancement method proposed in this paper has excellent performance.

Cloud detection in remote sensing images is a crucial preprocessing step that efficiently identifies and extracts cloud-covered areas within the images, ensuring the precision and reliability of subsequent analyses and applications. Given the diversity of clouds and the intricacies of the surface, distinguishing the boundaries between thin clouds and the underlying surface is a major challenge in cloud detection. To address these challenges, an advanced cloud detection method, CloudformerV3, is presented in this paper. The proposed method employs a multi-scale adapter to incorporate dark and bright channel prior information into the model’s backbone, enhancing the model’s ability to capture prior information and multi-scale details from remote sensing images. Additionally, multi-level large window attention is utilized, enabling high-resolution feature maps and low-resolution feature maps to mutually focus and subsequently merge during the resolution recovery phase. This facilitates the establishment of connections between different levels of feature maps and offers comprehensive contextual information for the model’s decoder. The experimental results on the GF1_WHU dataset illustrate that the method proposed in this paper achieves MIoU of 92.89%, while achieving higher detection accuracy compared to state-of-the-art cloud detection models. Specifically, in comparison to Cloudformer, our method demonstrates a 1.11% improvement, while compared to CloudformerV2, there is a 0.37% increase. Furthermore, enhanced detection performance is achieved along cloud edges and concerning thin clouds, showcasing the efficacy of the proposed method.

Cloud detection in remote sensing images is a crucial preprocessing step that efficiently identifies and extracts cloud-covered areas within the images, ensuring the precision and reliability of subsequent analyses and applications. Given the diversity of clouds and the intricacies of the surface, distinguishing the boundaries between thin clouds and the underlying surface is a major challenge in cloud detection. To address these challenges, an advanced cloud detection method, CloudformerV3, is presented in this paper. The proposed method employs a multi-scale adapter to incorporate dark and bright channel prior information into the model's backbone, enhancing the model's ability to capture prior information and multi-scale details from remote sensing images. Additionally, multi-level large window attention is utilized, enabling high-resolution feature maps and low-resolution feature maps to mutually focus and subsequently merge during the resolution recovery phase. This facilitates the establishment of connections between different levels of feature maps and offers comprehensive contextual information for the model's decoder. Experimental results on the GF1_WHU dataset demonstrate that the method introduced in this paper exhibits superior detection accuracy when compared to state-of-the-art cloud detection models. Furthermore, enhanced detection performance is achieved along cloud edges and with respect to thin clouds, showcasing the efficacy of the proposed method.

With increasing demand from unmanned driving and robotics, more attention has been paid to point cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features about reflectivity of the point cloud to maximizing the use of information in point cloud. Secondly, we enhanced the distance features and raw features that we collectively refer to them as self-features of the key points in Set Abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show the enhancement method proposed in this paper has excellent performance.

In driverless systems (scenarios such as subways, buses, trucks, etc.), multi-modal data fusion, such as light detection and ranging (LiDAR) points and camera images, is essential for accurate 3D object detection. In the fusion process, the information interaction between the modes is challenging due to the different coordinate systems of various sensors and the significant difference in the density of the collected data. It is necessary to fully consider the consistency and complementarity of multi-modal information, make up for the gap between multi-source data density, and achieve the joint interactive processing of multi-source information. Therefore, this paper is based on Transformer to improve a new multi-modal fusion model called PIDFusion for 3D object detection. Firstly, the method uses the results of 2D instance segmentation to generate dense 3D virtual points to enhance the original sparse 3D point clouds. This optimizes the issue that the nearest Euclidean distance in the 2D image space cannot ensure the nearest in the 3D space. Secondly, a new cross-modal fusion architecture is designed to maintain individual per-modality features to take advantage of their unique characteristics during 3D object detection. Finally, an instance-level fusion module is proposed to enhance semantic consistency through cross-modal feature interaction. Experiments show that PIDFusion is far ahead of existing 3D object detection methods, especially for small and long-range objects, with 70.8 mAP and 73.5 NDS on the nuScenes test set.