IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detection

• URL: http://arxiv.org/abs/2511.20319v1
• Date: Tue, 25 Nov 2025 13:53:54 GMT
• Title: IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detection
• Authors: Xuelin Qian, Jiaming Lu, Zixuan Wang, Wenxuan Wang, Zhongling Huang, Dingwen Zhang, Junwei Han,
• Abstract summary: IrisNet is a novel meta-learned framework that adapts detection strategies to the input infrared image status.<n>Our approach establishes a dynamic mapping between infrared image features and entire decoder parameters.<n> Experiments on NUDT-SIRST, NUAA-SIRST, and IRSTD-1K datasets demonstrate the superiority of our IrisNet.
• Score: 92.56025546608699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared Small Target Detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, complex backgrounds, and the absence of discernible target features. While deep learning-based encoder-decoder frameworks have advanced the field, their static pattern learning suffers from pattern drift across diverse scenarios (\emph{e.g.}, day/night variations, sky/maritime/ground domains), limiting robustness. To address this, we propose IrisNet, a novel meta-learned framework that dynamically adapts detection strategies to the input infrared image status. Our approach establishes a dynamic mapping between infrared image features and entire decoder parameters via an image-to-decoder transformer. More concretely, we represent the parameterized decoder as a structured 2D tensor preserving hierarchical layer correlations and enable the transformer to model inter-layer dependencies through self-attention while generating adaptive decoding patterns via cross-attention. To further enhance the perception ability of infrared images, we integrate high-frequency components to supplement target-position and scene-edge information. Experiments on NUDT-SIRST, NUAA-SIRST, and IRSTD-1K datasets demonstrate the superiority of our IrisNet, achieving state-of-the-art performance.

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