Motion-Enhanced Nonlocal Similarity Implicit Neural Representation for Infrared Dim and Small Target Detection

• URL: http://arxiv.org/abs/2504.15665v1
• Date: Tue, 22 Apr 2025 07:42:00 GMT
• Title: Motion-Enhanced Nonlocal Similarity Implicit Neural Representation for Infrared Dim and Small Target Detection
• Authors: Pei Liu, Yisi Luo, Wenzhen Wang, Xiangyong Cao,
• Abstract summary: Infrared dim and small target detection presents a significant challenge due to dynamic multi-frame scenarios and weak target signatures.<n>Traditional low-rank plus sparse models often fail to capture dynamic backgrounds and global spatial-temporal correlations.<n>We propose a novel motion-enhanced nonlocal similarity implicit neural representation framework to address these challenges.
• Score: 9.459649691992377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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