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.
Abstract (translated)
红外低信噪比小目标检测由于动态多帧场景和弱目标特征，在红外模式下面临重大挑战。传统的低秩加稀疏模型常常无法捕捉动态背景以及全局时空相关性，从而导致背景泄露或目标丢失的问题。在本文中，我们提出了一种新的基于运动增强的非局部相似性的隐式神经网络表示（INR）框架来应对这些挑战。 首先，我们将通过光流估计的方式整合运动估算以捕获细微的目标移动，并提出了多帧融合技术来增强运动显著性。其次，我们利用非局部相似性构造具有强低秩性质的补丁张量，并提出了一种基于张量分解的创新INR模型来表示这些非局部补丁张量，通过连续神经网络表示有效地编码背景中的非局部低秩性和时空相关性。 为了对非局部INR模型进行优化，我们开发了一种交替方向乘子法（ADMM），该方法具有理论上的固定点收敛特性。实验结果表明，我们的方法能够稳健地将微弱目标从复杂的红外背景中分离出来，在检测准确率和鲁棒性方面优于现有的最先进技术。
URL
https://arxiv.org/abs/2504.15665
