The content discusses the challenges of detecting small infrared targets due to low signal-to-clutter ratio and proposes a generative approach using diffusion models. The method compensates for false alarms and missed detections, achieving competitive performance on various datasets. The Low-frequency Isolation module in the wavelet domain reduces interference from infrared noise, enhancing detection accuracy.
Key points include the introduction of Infrared Small Target Detection (IRSTD), challenges faced by existing methods, proposal of a diffusion model framework, design of a Low-frequency Isolation module, contributions of the proposed method, related work in IRSTD, and comparison with state-of-the-art methods.
The experiments conducted on three datasets demonstrate the effectiveness of the proposed IRSTD-Diff method in improving target-level sensitivity and reducing false alarms. The ablation studies confirm the importance of both Conditional Encoder (CE) and Low-frequency Isolation in the Wavelet domain (LIW) modules in enhancing performance.
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by Haoqing Li,J... في arxiv.org 03-14-2024
https://arxiv.org/pdf/2403.08380.pdfاستفسارات أعمق