用深度去噪器提升高光谱异常检测精度，抗噪能力强于现有方法。
Provably Convergent Plug-and-play Proximal Block Coordinate Descent Method for Hyperspectral Anomaly Detection
- 通过低秩表示与深度去噪器结合，有效分离背景与异常像素。
- 在含噪和无噪场景下均优于对比方法，误检率显著降低。
- 适合高光谱图像中需精准识别异常目标的场景，如军事侦察、环境监测。
高光谱异常检测旨在识别光谱特征明显偏离背景的像素。本文提出一种新模型，利用低秩表示刻画背景信息，并在即插即用（PnP）框架中引入隐式近似去噪先验，结合基于深度学习的去噪器，有效去除与低秩表示相关的特征图像中的噪声。异常通过广义组稀疏度量 $\\-\cdot\|_{2,ψ}$ 表示。为求解由此产生的正交约束非凸非光滑优化问题，提出一种PnP-近似块坐标下降（PnP-PBCD）方法，其中特征图像通过PnP框架内的近似去噪器更新。理论证明：PnP-PBCD生成序列的任意聚点均为驻点。在含高斯噪声和无噪声条件下评估该方法，结果表明其能有效检测异常物体，优于其他可能将噪声误判为异常或受噪声干扰而误判目标的方法。
原文摘要 · Abstract (English)
Hyperspectral anomaly detection refers to identifying pixels in the hyperspectral images that have spectral characteristics significantly different from the background. In this paper, we introduce a novel model that represents the background information using a low-rank representation. We integrate an implicit proximal denoiser prior, associated with a deep learning based denoiser, within a plug-and-play (PnP) framework to effectively remove noise from the eigenimages linked to the low-rank representation. Anomalies are characterized using a generalized group sparsity measure, denoted as $\|\cdot\|_{2,ψ}$. To solve the resulting orthogonal constrained nonconvex nonsmooth optimization problem, we develop a PnP-proximal block coordinate descent (PnP-PBCD) method, where the eigenimages are updated using a proximal denoiser within the PnP framework. We prove that any accumulation point of the sequence generated by the PnP-PBCD method is a stationary point. We evaluate the effectiveness of the PnP-PBCD method on hyperspectral anomaly detection in scenarios with and without Gaussian noise contamination. The results demonstrate that the proposed method can effectively detect anomalous objects, outperforming the competing methods that may mistakenly identify noise as anomalies or misidentify the anomalous objects due to noise interference.
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