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English(EN) Seeing through Denoising: Frequency-guided One-step Denoising for Infrared Small Target Detection

新的生成框架DiffIRSTD增强红外小目标检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的红外小目标检测（IRSTD）生成框架DiffIRSTD。该方法将问题重新构建为背景重建，将小目标视为噪声信号，并通过基于扩散的模型将其去除。重建背景与原始图像之间的残差突出了潜在的目标区域。为了提高效率，采用了单步去噪策略，并使用频率引导感知损失来保留高频细节。DiffIRSTD被设计为一个即插即用模块，可以在不增加计算复杂性的情况下增强现有的IRSTD模型，并在多个数据集上展示了最先进的性能。 AI

影响 这种用于IRSTD的生成方法有望在具有挑战性的视觉环境中提高检测的准确性和效率。

排序理由 该项目是一篇研究论文，详细介绍了一种用于红外小目标检测的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的生成框架DiffIRSTD增强红外小目标检测

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该项目是一篇研究论文，详细介绍了一种用于红外小目标检测的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xuelin Qian, Zixuan Wang, Chang Xu, Dingwen Zhang, Junwei Han ·

    Seeing through Denoising: Frequency-guided One-step Denoising for Infrared Small Target Detection

    arXiv:2610.29361v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) is a challenging task due to the extremely small size of targets, low signal-to-noise ratios, and heavy background clutter in infrared imagery. While the progress of deep learning has advanced…

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