It's Not the Target, It's the Background: Rethinking Infrared Small Target Detection via Deep Patch-Free Low-Rank Representations
Journal:
arXiv
Published Date:
Jun 12, 2025
Abstract
Infrared small target detection (IRSTD) remains a long-standing challenge in
complex backgrounds due to low signal-to-clutter ratios (SCR), diverse target
morphologies, and the absence of distinctive visual cues. While recent deep
learning approaches aim to learn discriminative representations, the intrinsic
variability and weak priors of small targets often lead to unstable
performance. In this paper, we propose a novel end-to-end IRSTD framework,
termed LRRNet, which leverages the low-rank property of infrared image
backgrounds. Inspired by the physical compressibility of cluttered scenes, our
approach adopts a compression--reconstruction--subtraction (CRS) paradigm to
directly model structure-aware low-rank background representations in the image
domain, without relying on patch-based processing or explicit matrix
decomposition. To the best of our knowledge, this is the first work to directly
learn low-rank background structures using deep neural networks in an
end-to-end manner. Extensive experiments on multiple public datasets
demonstrate that LRRNet outperforms 38 state-of-the-art methods in terms of
detection accuracy, robustness, and computational efficiency. Remarkably, it
achieves real-time performance with an average speed of 82.34 FPS. Evaluations
on the challenging NoisySIRST dataset further confirm the model's resilience to
sensor noise. The source code will be made publicly available upon acceptance.
