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OMA-SSR: Optical-guided multi-kernel attention based SAR image super-resolution reconstruction network

IET IMAGE procESSING 2025年第1期19卷

作者： Li, Yanshan Xu, Fan Shenzhen Univ Inst Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China

Synthetic aperture radar (SAR) has been widely studied and applied in many fields. Although image super-resolution technology has been successfully applied to SAR imaging in recent years, there is less research on large-scale factor SAR image super-resolution methods. A more effective method is to obtain comprehensive information to guide the reconstruction of SAR images. In fact, the co-registered characteristics of high-resolution optical images have been successfully applied to improve the quality of SAR images. Inspired by this, an optical-guided multi-kernel attention based SAR image super-resolution reconstruction network (OMA-SSR) is proposed. The proposed multi-modal mutual attention (MMA) module in this network can effectively establish the dependency between SAR image features and optical image features. This network also designs a deep feature extraction module for SAR images, which includes a channel-splitted multi-kernel attention (CSMA) module and residual connections. CSMA module splits SAR image channels, extracts features in different ranges through multi-kernel convolution, and finally fuses the extracted features between different channels. Experimental results on the Sen1-2 and QXS datasets show that the proposed OMA-SSR performs well in evaluation indicators and visual effects of SAR image super-resolution reconstruction.

关键词： computer vision image reconstruction

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An Interaction-Scene Collaborative Representation Framework for Detecting Traffic Anomalies in Driving Videos

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2025年

作者： Liang, Rongqin Li, Yuanman Wu, Zhenyu Li, Xia Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

Accurate traffic anomaly detection (TAD) is critical for intelligent transportation systems. Previous TAD methods mainly relied on driving scene perception or motion patterns of agents (i.e., traffic participants, such as vehicles and pedestrians) to detect traffic anomalies. Although these methods achieve promising detection performance, they lack an intuitive modeling of agent interactions, which limits their ability to handle complex driving scenarios. In fact, modeling interactions among agents helps to understand the underlying logic behind changes in agent behavior, thereby benefiting traffic anomaly detection. In our work, we introduce Interaction-Scene Collaborative Representation for Traffic Anomaly Detection (ISCRTAD), an innovative framework that leverages advanced artificial intelligence techniques to model agent interactions in dynamic driving scenarios. Unlike previous TAD methods, the proposed approach is the first try to collaboratively represent agent interactions and dynamic driving scenarios, significantly enhancing the perception and understanding of traffic anomalies in driving videos. First, we introduce the agent interaction modeling module, which comprehensively models the interactions between agents in driving scenarios through the designed Behavior Interaction Graph and Spatial Perception Graph. Furthermore, we design a heterogeneous modality collaborative representation (HMCR) mechanism to deeply integrate agent interactions with dynamic driving scenarios, thereby enabling a more profound understanding of agent motion patterns in dynamic driving environments. Experimental results on the DoTA and DADA datasets demonstrate significant improvements in traffic anomaly detection performance, highlighting the effectiveness of our AI-driven approach.

关键词： Videos Anomaly detection Feature extraction Vehicle dynamics Collaboration Dynamics Visualization Training Surveillance intelligent transportation systems Traffic anomaly detection interaction multi-modality modeling intelligent transportation system generative artificial intelligence

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Dual prior guided depth image super-resolution with multi-scale transformer fusion network

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VISUAL COMPUTER 2025年第8期41卷 5919-5933页

作者： Zhao, Pengfei Ji, Jianhua Wen, Yang Shi, Wuzhen Cao, Wenming Shenzhen Univ Coll Elect & Informat Engn Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen Peoples R China

Guided depth super-resolution (GDSR) is a challenging task that aims to restore a high-resolution depth map from a low-resolution one, using a high-resolution RGB image of the same scene as guidance. Recently, most existing GDSR methods have been limited by insufficient prior knowledge and inappropriate guidance, leading to several challenges, such as serious boundary discontinuities, incomplete structures, and unsatisfactory colors in the reconstructed high-resolution depth maps. To overcome these limitations, we propose a dual prior guided depth image super-resolution method using a multi-scale transformer fusion network. Firstly, we design a dual prior mechanism by employing a color branch to extract color priors from RGB images and an edge branch to extract edge priors from depth images. This approach provides effective prior knowledge for high-resolution depth images, resulting in complete structures and satisfactory colors. Meanwhile, we introduce the self-attention mechanism of the Transformer to the guided depth map super-resolution task to extract global features through a transformer block that utilizes feature mapping from a semi-coupled convolutional block. In addition, we introduce a multi-scale feature fusion module to maintain the boundary continuity of the reconstructed depth image by enhancing the depth image super-resolution guided by RGB images from multiple scales. Extensive quantitative and qualitative experiments on multiple datasets demonstrate that the proposed method has significant advantages over other state-of-the-art GDSR methods. In particular, our method achieves superior super-resolution performance on depth images with noise.

关键词： Guided depth super-resolution Multi-scale fusion Dual prior guidance Transformer block

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Dual stream fusion module integrating 2D and 3D for 3D semi-supervised medical image segmentation

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SIGNAL IMAGE AND VIDEO procESSING 2025年第4期19卷 1-11页

作者： He, Zhiquan Ouyang, Yating Wen, Yang Shenzhen Univ Guangdong Prov Key Lab Intelligent Informat Proc Nanhai Blvd Shenzhen 518060 Peoples R China

Semi-supervised learning has proven effective in the challenging field of 3D medical image segmentation. However, existing methods typically focus solely on utilizing 3D features, which limits their ability to capture local differences effectively. To address this limitation, we propose a Dual Stream Fusion Model (DSFM) that fuses both 3D and 2D features, enabling the model to capture both global and local information. The model consists of a Cross-Dimensional Attention Fusion Module (CDAFM) and a Self-Adaptive Fusion Module (SAFM). The CDAFM integrates 3D and 2D features using an attention mechanism, while the SAFM merges the segmentation results from both modalities through adaptive weighting. Together, these modules enhance the model's ability to focus on local differences while simultaneously learning global features. Specifically, the 3D decoder receives fused features from both the 3D and 2D encoders, and the final segmentation result is produced by combining the outputs of both the 3D and 2D decoders. Furthermore, we introduce an alignment loss to enforce consistency between the 3D and 2D segmentation sub-networks through regularization. Comprehensive experiments on four publicly available 3D medical datasets demonstrate the effectiveness of our approach. Notably, on the BRaTS19 dataset, which contains only 20% annotated images, we observe a 2.83% improvement in the Jaccard index.

关键词： Dual stream fusion Joint 3D and 2D learning Semi-supervised learning Medical image segmentation

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Dual-Stream Image Sharing Chain Detection via Dynamic information Compensation

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IEEE SIGNAL procESSING LETTERS 2025年 32卷 1311-1315页

作者： Su, Xinyi Li, Yuanman Zheng, Yulong Li, Xia Shenzhen Univ Coll Elect & Informat Engn Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

Image Sharing Chain Detection (ISCD) aims to reconstruct the complete trajectory of an image's dissemination across social platforms and is an important task in multimedia forensics. Current methods using DCT histograms are insufficient in uncovering platform compression traces and exhibit limitations in detecting weak trace platforms. In this letter, we propose an innovative dual-stream ISCD framework via dynamic information compensation. This framework integrates features from both the frequency domain and the residual domain to extract compression characteristics. Unlike existing methods, we employ binary stereo DCT in the frequency domain to focus on the spatiality of compression operations. Additionally, we design a dynamic information compensation mechanism to enhance platform traces by storing compensation fingerprints of the sharing chains. Furthermore, we develop a new dataset, F-4OSN-SC, encompassing 4 platforms to simulate more realistic social networking scenarios. Experimental results demonstrate that our model outperforms existing methods across multiple datasets.

关键词： Discrete cosine transforms Feature extraction Image coding Fingerprint recognition Noise Frequency-domain analysis Training Histograms Data mining Social networking (online) Image forensics image sharing chain detection dynamic information compensation online social platforms

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Evading Detection Actively: Toward Anti-Forensics Against Forgery Localization

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2025年第2期22卷 852-869页

作者： Zhuo, Long Luo, Shenghai Tan, Shunquan Chen, Han Li, Bin Huang, Jiwu Shenzhen MSU BIT Univ Guangdong Lab Machine Percept & Intelligent Comp Fac Engn Shenzhen 518116 Peoples R China Shenzhen Key Lab Media Secur Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Guangdong Peoples R China

Anti-forensics seeks to eliminate or conceal traces of tampering artifacts. Typically, anti-forensic methods are designed to deceive binary detectors and persuade them to misjudge the authenticity of an image. However, to the best of our knowledge, no attempts have been made to deceive forgery detectors at the pixel level and mis-locate forged regions. Traditional adversarial attack methods cannot be directly used against forgery localization due to the following defects: 1) they tend to just naively induce the target forensic models to flip their pixel-level pristine or forged decisions;2) their anti-forensics performance tends to be severely degraded when faced with the unseen forensic models;3) they lose validity once the target forensic models are retrained with the anti-forensics images generated by them. To tackle the three defects, we propose SEAR (Self-supErvised Anti-foRensics), a novel self-supervised and adversarial training algorithm that effectively trains deep-learning anti-forensic models against forgery localization. SEAR sets a pretext task to reconstruct perturbation for self-supervised learning. In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces. We have conducted large-scale experiments across diverse datasets. The experimental results demonstrate that, through the combination of self-supervised learning and adversarial learning, SEAR successfully deceives the state-of-the-art forgery localization methods, as well as tackle the three defects regarding traditional adversarial attack methods mentioned above.

关键词： Location awareness Training Forensics Perturbation methods Computational modeling Self-supervised learning Detectors Forgery Adversarial machine learning Robustness Anti-forensics concealer supervisor self-supervised training adversarial learning

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Joint super-resolution-based fast face image coding for human and machine vision

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VISUAL COMPUTER 2025年第2期41卷 1395-1408页

作者： Shi, Wuzhen Tao, Fei Wen, Yang Shenzhen Univ Coll Elect & Informat Engn Guangdong Prov Engn Lab Digital Creat Technol Guangdong Prov Key Lab Intelligent Informat Proc 3688 Nanhai Ave Shenzhen Peoples R China

As the Internet of Things continues to grow and thrive, more and more data are consumed by machines for intelligent analysis. How to simultaneously support fast machine vision analysis and obtain high-quality reconstructed images to serve human vision has become a problem that needs to be solved. We propose a fast face image compression scheme based on a Joint Super-Resolution Network (JSRNet), where images are encoded hierarchically to support machine vision and human vision. To support fast machine vision tasks, we compress the downsampled version of the input image at the encoding side and implement base layer decoding at the decoding side with a lightweight super-resolution network, thus effectively reducing the codec time. To obtain high-quality reconstructed images, we design a content transfer module to transfer the visual information generated by the base layer to the enhancement layer. The joint optimization of the base layer and the enhancement layer also allows them to better cooperate to achieve better performance. We verify the performance of this scheme on the facial landmark detection task, and the experimental results show that our method saves more bit-rate and achieves more accurate facial landmark detection results with faster codec time than baselines. The reconstructed images are more in line with human visual characteristics, and the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are also improved significantly.

关键词： Scalable coding Image compression Video coding for machine Super-resolution

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Deep Fuzzy System for Dual-Station Target Tracking With Azimuth and Doppler Measurements

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第4期33卷 1287-1297页

作者： Huang, Jianjun Geng, Xuehao Kang, Li Luo, Ge Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518061 Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen 518061 Peoples R China

To address the issues of excessive estimation error and unstable filtering caused by uncertainties in process noise and system models, we propose a Wang-Mendel fuzzy system (WMFS)-based unscented Kalman filter (UKF) algorithm for dual-station target tracking with azimuth and Doppler measurements. The algorithm leverages the strengths of WMFS in handling system uncertainties and complex modeling. During the derivation of the WMFS-UKF, the unscented transform (UT) framework is employed to tackle the nonlinear measurement problem, while the state transition function is reconstructed using a pretrained WMFS. By utilizing the historical states of the target and the expected outputs, the WM fuzzy inference system achieves more accurate state predictions and precise covariance estimates. This leads to significantly improved performance and enhanced stability in target tracking. Simulation experiments and real-data filtering experiment validate the algorithm's effectiveness and robustness in various target tracking scenarios.

关键词： Fuzzy systems Target tracking Uncertainty Filtering Mathematical models Kalman filters Doppler effect Adaptation models Accuracy Noise Azimuth and Doppler uncertain models unscented filtering Wang-Mendel fuzzy model

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MRI super-resolution reconstruction based on 3D dense connected convolutional autoencoders

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BIOMEDICAL SIGNAL procESSING AND CONTROL 2025年 107卷

作者： Kang, Li Song, Xinxin Huang, Jianjun Liu, Guojuan Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

High-Resolution MR (HR-MR) images provide rich structural detail to assist physicians in clinical diagnosis and detection of lesions. However, it is arduous to acquire HR-MRI due to equipment limitations, scanning time or patient comfort. Computer-aided image post-processing approaches allow indirect acquisition of HR-MRI without the requirements described above. In this paper, we propose a MRI super-resolution reconstruction method based on an image post-processing technique. Specifically, 3D densely connected convolutional neural networks are introduced to address the problem of underutilizing the spatial information in the reconstruction of 2D slices by convolutional neural networks, which can simultaneously achieve the super- resolution reconstruction of three anatomical planes: axial, coronal and sagittal. To obtain features from different sensory fields, differently sized convolution kernels are used in the shallow feature extraction module, which promotes the robustness and reliability in the deep mapping process. Furthermore, an improved densely connected high frequency module (DBHE) is built to extract high-frequency features recovered from the deep mapping to avoid the loss of detailed information, while a hybrid attention module is utilized to boost the weights of important features. Several experiments are designed to evaluate the proposed framework, and the results suggest that it could achieve favorable reconstruction performance compared with state-of-the-art methods. In addition, the experimental results on other datasets also show the proposed framework's promising generalization performance.

关键词： CNN MRI Encoder-decoder Attention

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A progressive segmentation network for navigable areas with semantic-spatial information flow

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Li, Wei Liao, Muxin Zou, Wenbin Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Guangdong Peoples R China

Segmentation of safe navigable areas is a crucial technology for scene parsing in autopilot systems. However, existing segmentation methods often fail to adequately exploit the complementary relationships between multiscale features in complex wild environments, resulting in insufficient information fusion. In pursuit of a resolution, a Progressive Segmentation Network (PSNet) is proposed for navigable areas segmentation, which builds a semantic-spatial information flow branch to dynamically exploit the complementary relationships between multiscale features for progressive guided learning. Specifically, PSNet consists of four essential modules, including the Local Capturer and Global dependence Builder (LCGB), Multi-Directional Pooling Module (MDPM), Fusion-wise Module (FWM), and Spatial Weight Aggregation Module (SWAM). To facilitate efficient information dissemination, the LCGB was utilized to capture dense spatial information, and the MDPM was proposed to extract global geometric information of obstacles. These pieces of information serve as prior knowledge to guide learning. Additionally, we propose the FWM based on attention fusion unit (AFU) and contribution weights unit (CWU) to construct the complementary relationships between multiscale features and obtain rich multiscale fusion information. SWAM is proposed to enhance the significant spatial features from FWM and achieve impressive segmentation results. Extensive experimental results on the wild datasets demonstrate that PSNet outperforms state-of-the-art methods in recognizing safe navigable areas. The code for PSNet will be available at https://***/lv881314/PSNet.

关键词： Navigable areas Deep neural networks Auxiliary information Semantic-spatial information flow Spatial weight aggregation

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