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学者姓名：裘兆炳
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针对现有图像超分辨率重建方法存在模型复杂度过高和参数量过大等问题,文中提出基于多尺度空间自适应注意力网络(Multi-scale Spatial Adaptive Attention Network,MSAAN)的轻量级图像超分辨率重建方法.首先,设计全局特征调制模块(Global Feature Modulation Module,GFM),学习全局纹理特征.同时,设计轻量级的多尺度特征聚合模块(Multi-scale Feature Aggregation Module,MFA),自适应聚合局部至全局的高频空间特征.然后,融合GFM和MFA,提出多尺度空间自适应注意力模块(Multi-scale Spatial Adaptive Attention Module,MSAA).最后,通过特征交互门控前馈模块(Feature Interactive Gated Feed-Forward Module,FIGFF)增强局部信息提取能力,同时减少通道冗余.大量实验表明,MSAAN能捕捉更全面、更精细的特征,在保证轻量化的同时显著提升图像的重建效果.
Keyword ：
Transformer Transformer 卷积神经网络 卷积神经网络 多尺度空间自适应注意力 多尺度空间自适应注意力 轻量级图像超分辨率重建 轻量级图像超分辨率重建
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| GB/T 7714 | 黄峰 , 刘鸿伟 , 沈英 et al. 基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法 [J]. | 模式识别与人工智能 , 2025 , 38 (1) : 36-50 . |
| MLA | 黄峰 et al. "基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法" . | 模式识别与人工智能 38 . 1 (2025) : 36-50 . |
| APA | 黄峰 , 刘鸿伟 , 沈英 , 裘兆炳 , 陈丽琼 . 基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法 . | 模式识别与人工智能 , 2025 , 38 (1) , 36-50 . |
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Infrared (IR) small target detection exerts a significant role in IR early warning and UAV surveillance. However, in the low-altitude slow-speed small (LSS) target detection scene, the existing algorithms cannot effectively suppress high-contrast corners and sparse edges in the low-altitude background, resulting in many false alarms. To solve this problem, we propose an IR LSS target detection method based on fusion of target sparsity and motion saliency (TSMS). In the low-rank sparse model, we introduce a robust dual-window gradient operator to construct a fine local prior, which avoids the influence of highlighted edges and corners; The Geman norm is used to approximate the background rank to accurately estimate the background and effectively extract sparse targets. Then, a motion saliency model based on inter-frame local matching is constructed to accurately extract the inter-frame features of small target. Finally, the real LSS target is obtained by fusing target sparsity and motion saliency. Experiments indicate that, compared with existing advanced methods, the proposed method has stronger robustness and can effectively detect LSS targets under complex low-altitude background. © 2024 Elsevier B.V.
Keyword ：
Infrared (IR) image Infrared (IR) image Low-rank sparse Low-rank sparse Motion saliency Motion saliency Prior weight Prior weight Small target detection Small target detection
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| GB/T 7714 | Wu, L. , Ma, Y. , Huang, J. et al. Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency [J]. | Infrared Physics and Technology , 2024 , 142 . |
| MLA | Wu, L. et al. "Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency" . | Infrared Physics and Technology 142 (2024) . |
| APA | Wu, L. , Ma, Y. , Huang, J. , Qiu, Z. , Fan, F. . Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency . | Infrared Physics and Technology , 2024 , 142 . |
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The limited availability of thermal infrared (TIR) training samples leads to suboptimal target representation by convolutional feature extraction networks, which adversely impacts the accuracy of TIR target tracking methods. To address this issue, we propose an unsupervised cross-domain model (UCDT) for TIR tracking. Our approach leverages labeled training samples from the RGB domain (source domain) to train a general feature extraction network. We then employ a cross-domain model to adapt this network for effective target feature extraction in the TIR domain (target domain). This cross-domain strategy addresses the challenge of limited TIR training samples effectively. Additionally, we utilize an unsupervised learning technique to generate pseudo-labels for unlabeled training samples in the source domain, which helps overcome the limitations imposed by the scarcity of annotated training data. Extensive experiments demonstrate that our UCDT tracking method outperforms existing tracking approaches on the PTB-TIR and LSOTB-TIR benchmarks.
Keyword ：
cross-domain model cross-domain model feature extraction feature extraction thermal infrared tracking thermal infrared tracking unsupervised learning unsupervised learning
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| GB/T 7714 | Shu, Xiu , Huang, Feng , Qiu, Zhaobing et al. Learning Unsupervised Cross-Domain Model for TIR Target Tracking [J]. | MATHEMATICS , 2024 , 12 (18) . |
| MLA | Shu, Xiu et al. "Learning Unsupervised Cross-Domain Model for TIR Target Tracking" . | MATHEMATICS 12 . 18 (2024) . |
| APA | Shu, Xiu , Huang, Feng , Qiu, Zhaobing , Zhang, Xinming , Yuan, Di . Learning Unsupervised Cross-Domain Model for TIR Target Tracking . | MATHEMATICS , 2024 , 12 (18) . |
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Thermal infrared (TIR) target tracking is an important topic in the computer vision area. The TIR images are not affected by ambient light and have strong environmental adaptability, making them widely used in battlefield perception, video surveillance, and assisted driving. However, TIR target tracking faces problems such as relatively insufficient information and lack of target texture information, which significantly affects the tracking accuracy of the TIR tracking methods. To solve the above problems, we propose a TIR target tracking method based on a Siamese network with a hierarchical attention mechanism (called: SiamHAN). Specifically, the CIoU Loss is introduced to make full use of the regression box information to calculate the loss function more accurately. The global context network (GCNet) attention mechanism is introduced to reconstruct the feature extraction structure of fine-grained information for the fine-grained information of TIR images. Meanwhile, for the feature information of the hierarchical backbone network of the Siamese network, the ECANet attention mechanism is used for hierarchical feature fusion, so that it can fully utilize the feature information of the multilayer backbone network to represent the target. On the LSOTB-TIR, the hierarchical attention Siamese network achieved a 2.9% increase in success rate and a 4.3% increase in precision relative to the baseline tracker. Experiments show that the proposed SiamHAN method has achieved competitive tracking results on the TIR testing datasets.
Keyword ：
Accuracy Accuracy Attention mechanism Attention mechanism Convolution Convolution feature extraction feature extraction Feature extraction Feature extraction feature fusion feature fusion Interference Interference Siamese network Siamese network Support vector machines Support vector machines Target tracking Target tracking thermal infrared (TIR) target tracking thermal infrared (TIR) target tracking Training Training
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| GB/T 7714 | Yuan, Di , Liao, Donghai , Huang, Feng et al. Hierarchical Attention Siamese Network for Thermal Infrared Target Tracking [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Yuan, Di et al. "Hierarchical Attention Siamese Network for Thermal Infrared Target Tracking" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Yuan, Di , Liao, Donghai , Huang, Feng , Qiu, Zhaobing , Shu, Xiu , Tian, Chunwei et al. Hierarchical Attention Siamese Network for Thermal Infrared Target Tracking . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
Infrared (IR) small target detection exerts a significant role in IR early warning and UAV surveillance. However, in the low-altitude slow-speed small (LSS) target detection scene, the existing algorithms cannot effectively suppress high-contrast corners and sparse edges in the low-altitude background, resulting in many false alarms. To solve this problem, we propose an IR LSS target detection method based on fusion of target sparsity and motion saliency (TSMS). In the low-rank sparse model, we introduce a robust dual-window gradient operator to construct a fine local prior, which avoids the influence of highlighted edges and corners; The Geman norm is used to approximate the background rank to accurately estimate the background and effectively extract sparse targets. Then, a motion saliency model based on inter-frame local matching is constructed to accurately extract the inter frame features of small target. Finally, the real LSS target is obtained by fusing target sparsity and motion saliency. Experiments indicate that, compared with existing advanced methods, the proposed method has stronger robustness and can effectively detect LSS targets under complex low-altitude background.
Keyword ：
Infrared (IR) image Infrared (IR) image Low-rank sparse Low-rank sparse Motion saliency Motion saliency Prior weight Prior weight Small target detection Small target detection
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| GB/T 7714 | Wu, Lang , Ma, Yong , Huang, Jun et al. Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency [J]. | INFRARED PHYSICS & TECHNOLOGY , 2024 , 142 . |
| MLA | Wu, Lang et al. "Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency" . | INFRARED PHYSICS & TECHNOLOGY 142 (2024) . |
| APA | Wu, Lang , Ma, Yong , Huang, Jun , Qiu, Zhaobing , Fan, Fan . Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency . | INFRARED PHYSICS & TECHNOLOGY , 2024 , 142 . |
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Abstract ：
Infrared small target detection is critical to infrared search and tracking systems. However, accurate and robust detection remains challenging due to the scarcity of target information and the complexity of clutter interference. Existing methods have some limitations in feature representation, leading to poor detection performance in complex scenes. Especially when there are sharp edges near the target or in cluster multitarget detection, the "target suppression" phenomenon tends to occur. To address this issue, we propose a robust unsupervised multifeature representation (RUMFR) method for infrared small target detection. On the one hand, robust unsupervised spatial clustering (RUSC) is designed to improve the accuracy of feature extraction; on the other hand, pixel-level multiple feature representation is proposed to fully utilize the target detail information. Specifically, we first propose the center-weighted interclass difference measure (CWIDM) with a trilayer design for fast candidate target extraction. Note that CWIDM also guides the parameter settings of RUSC. Then, the RUSC-based model is constructed to accurately extract target features in complex scenes. By designing the parameter adaptive strategy and iterative clustering strategy, RUSC can robustly segment cluster multitargets from complex backgrounds. Finally, RUMFR that fuses pixel-level contrast, distribution, and directional gradient features is proposed for better target representation and clutter suppression. Extensive experimental results show that our method has stronger feature representation capability and achieves better detection performance than several state-of-the-art methods.
Keyword ：
Clutter Clutter Feature extraction Feature extraction Fuses Fuses Image edge detection Image edge detection Infrared small target detection Infrared small target detection Noise Noise Object detection Object detection pixel-level multifeature representation pixel-level multifeature representation robust unsupervised spatial clustering (RUSC) robust unsupervised spatial clustering (RUSC) Sparse matrices Sparse matrices "target suppression" phenomenon "target suppression" phenomenon
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| GB/T 7714 | Chen, Liqiong , Wu, Tong , Zheng, Shuyuan et al. Robust Unsupervised Multifeature Representation for Infrared Small Target Detection [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 : 10306-10323 . |
| MLA | Chen, Liqiong et al. "Robust Unsupervised Multifeature Representation for Infrared Small Target Detection" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17 (2024) : 10306-10323 . |
| APA | Chen, Liqiong , Wu, Tong , Zheng, Shuyuan , Qiu, Zhaobing , Huang, Feng . Robust Unsupervised Multifeature Representation for Infrared Small Target Detection . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 , 10306-10323 . |
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Due to their resilience against lighting variations, thermal infrared (TIR) images demonstrate robust adaptability in diverse environments, enabling effective object tracking even in intricate scenarios. Nevertheless, TIR target tracking encounters challenges such as fast target motion and interference from visually similar objects, substantially compromising the tracking precision of TIR trackers. To surmount these challenges, we propose a method grounded in the strategy of search region updating and hierarchical feature fusion, tailored for the precise TIR target- tracking task. Specifically, to address the issue of fast motion causing the target to depart from the search region, we propose to update the current search region by leveraging historical frame information. Additionally, we employ a hierarchical feature fusion strategy to contend with interference from visually similar objects in the tracking scenario. This strategy enhances the ability to model and represent the target more accurately, thereby elevating the tracker's capacity to discriminate between the target and similar objects. Furthermore, to tackle the challenge of inaccurate estimation of target bounding boxes, we introduce an enhanced Intersection over Union (IoU) loss function, which improvement facilitates a more precise prediction of target bounding boxes, resulting in superior target localization. Extensive experiments substantiate that our tracker exhibits a commendable level of competitiveness when compared to other trackers.
Keyword ：
Hierarchical feature fusion Hierarchical feature fusion IoU loss IoU loss Search region updating Search region updating TIR target tracking TIR target tracking
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| GB/T 7714 | Shu, Xiu , Huang, Feng , Qiu, Zhaobing et al. Search region updating with hierarchical feature fusion for accurate thermal infrared tracking [J]. | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS , 2024 , 361 (18) . |
| MLA | Shu, Xiu et al. "Search region updating with hierarchical feature fusion for accurate thermal infrared tracking" . | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 361 . 18 (2024) . |
| APA | Shu, Xiu , Huang, Feng , Qiu, Zhaobing , Tian, Chunwei , Liu, Qiao , Yuan, Di . Search region updating with hierarchical feature fusion for accurate thermal infrared tracking . | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS , 2024 , 361 (18) . |
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With the development of modern weapons such as UAV swarms and multiwarhead missiles, infrared (IR) cluster small target detection technology has become increasingly important. However, the difficulty in characterizing cluster multitargets leads to poor detection performance of existing methods. On the one hand, this letter proposes improved DBSCAN (IDBSCAN) to accurately extract the features of cluster multitargets with unknown numbers and distribution. On the other hand, an IDBSCAN-based difference measure (IDBSCAN-DM) is proposed, which fuses saliency and distribution features to further enhance cluster multitargets. Specifically, we first design the multiscale sliding window to quickly extract candidate targets. Then, the IDBSCAN-based local window is constructed and IDBSCAN-DM is computed for better target enhancement and background suppression. Finally, adaptive threshold segmentation is performed on the IDBSCAN-DM map to detect real targets. Extensive comparative experiments demonstrate that the proposed method achieves better target enhancement and a higher probability of detection.
Keyword ：
Feature extraction Feature extraction Geoscience and remote sensing Geoscience and remote sensing Gray-scale Gray-scale IDBSCAN-based difference measure (IDBSCAN-DM) IDBSCAN-based difference measure (IDBSCAN-DM) Image segmentation Image segmentation improved DBSCAN (IDBSCAN) improved DBSCAN (IDBSCAN) infrared (IR) cluster small target infrared (IR) cluster small target Object detection Object detection Robustness Robustness Shape Shape
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| GB/T 7714 | Qiu, Zhaobing , Ma, Yong , Fan, Fan et al. Improved DBSCAN for Infrared Cluster Small Target Detection [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2023 , 20 . |
| MLA | Qiu, Zhaobing et al. "Improved DBSCAN for Infrared Cluster Small Target Detection" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20 (2023) . |
| APA | Qiu, Zhaobing , Ma, Yong , Fan, Fan , Huang, Jun , Wu, Lang , Du, You . Improved DBSCAN for Infrared Cluster Small Target Detection . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2023 , 20 . |
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