Motion-Enhanced Nonlocal Similarity Implicit Neural Representation for Infrared Dim and Small Target Detection
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| Format: | Preprint |
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2025
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| _version_ | 1866914088888565760 |
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| author | Liu, Pei Luo, Yisi Wang, Wenzhen Cao, Xiangyong |
| author_facet | Liu, Pei Luo, Yisi Wang, Wenzhen Cao, Xiangyong |
| contents | Infrared dim and small target detection presents a significant challenge due to dynamic multi-frame scenarios and weak target signatures in the infrared modality. Traditional low-rank plus sparse models often fail to capture dynamic backgrounds and global spatial-temporal correlations, which results in background leakage or target loss. In this paper, we propose a novel motion-enhanced nonlocal similarity implicit neural representation (INR) framework to address these challenges. We first integrate motion estimation via optical flow to capture subtle target movements, and propose multi-frame fusion to enhance motion saliency. Second, we leverage nonlocal similarity to construct patch tensors with strong low-rank properties, and propose an innovative tensor decomposition-based INR model to represent the nonlocal patch tensor, effectively encoding both the nonlocal low-rankness and spatial-temporal correlations of background through continuous neural representations. An alternating direction method of multipliers is developed for the nonlocal INR model, which enjoys theoretical fixed-point convergence. Experimental results show that our approach robustly separates dim targets from complex infrared backgrounds, outperforming state-of-the-art methods in detection accuracy and robustness. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_15665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Motion-Enhanced Nonlocal Similarity Implicit Neural Representation for Infrared Dim and Small Target Detection Liu, Pei Luo, Yisi Wang, Wenzhen Cao, Xiangyong Computer Vision and Pattern Recognition Infrared dim and small target detection presents a significant challenge due to dynamic multi-frame scenarios and weak target signatures in the infrared modality. Traditional low-rank plus sparse models often fail to capture dynamic backgrounds and global spatial-temporal correlations, which results in background leakage or target loss. In this paper, we propose a novel motion-enhanced nonlocal similarity implicit neural representation (INR) framework to address these challenges. We first integrate motion estimation via optical flow to capture subtle target movements, and propose multi-frame fusion to enhance motion saliency. Second, we leverage nonlocal similarity to construct patch tensors with strong low-rank properties, and propose an innovative tensor decomposition-based INR model to represent the nonlocal patch tensor, effectively encoding both the nonlocal low-rankness and spatial-temporal correlations of background through continuous neural representations. An alternating direction method of multipliers is developed for the nonlocal INR model, which enjoys theoretical fixed-point convergence. Experimental results show that our approach robustly separates dim targets from complex infrared backgrounds, outperforming state-of-the-art methods in detection accuracy and robustness. |
| title | Motion-Enhanced Nonlocal Similarity Implicit Neural Representation for Infrared Dim and Small Target Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.15665 |