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

2025-04-22

AI Summary

Infrared small target detection suffers from severe dynamic background interference and extremely weak target features, leading to background leakage and target disappearance. To address these challenges, we propose a motion-enhanced non-local similarity-based implicit neural representation (INR) framework. First, optical flow estimation is employed to extract motion saliency, followed by multi-frame fusion to improve temporal consistency. Next, a non-local similarity patch tensor is constructed and decomposed via coupled INR, jointly encoding non-local low-rank structure and spatiotemporal continuity—marking the first such integration. Optimization is performed using the alternating direction method of multipliers (ADMM). Extensive experiments on multiple infrared datasets demonstrate that our method significantly outperforms state-of-the-art approaches, effectively suppressing background leakage, preventing target loss, and achieving superior detection accuracy and robustness in dynamic scenes.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Local Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Detecting dim small infrared targets in dynamic scenes
• Overcoming background leakage and target loss issues
• Enhancing motion saliency and spatial-temporal correlations

Innovation

• Motion-enhanced nonlocal similarity implicit neural representation
• Optical flow for subtle target movement estimation
• Tensor decomposition-based INR model for background correlation