将插件式算法展开为深度网络，提升高光谱解混的物理可解释性与性能。
Unrolling Plug-and-Play Network for Hyperspectral Unmixing
- 通过展开插件式算法构建深度架构，融合谱空间信息与预训练图像去噪先验。
- 在合成与真实数据集上均优于现有方法，显著提升解混精度。
- 适合需要可解释性与高性能的高光谱图像处理研究者使用。
近年来基于深度学习的解混方法受到广泛关注，取得了显著性能。这类方法采用数据驱动方式从高光谱图像中提取结构特征，但往往缺乏物理可解释性。传统解混方法虽更具可解释性，却需人工设计正则项并调整惩罚参数。为此，本文提出一种新型解混方法，通过展开插件式解混算法构建深度架构。该方法融合内部先验与外部先验：精心设计的展开深度网络用于学习高光谱图像中的光谱与空间信息（内部先验）；同时引入在大规模图像数据上预训练的深度去噪器，利用外部先验。此外，设计动态卷积以建模多尺度信息，并通过注意力模块融合不同尺度特征。在合成与真实数据集上的实验结果表明，该方法优于对比方法。
原文摘要 · Abstract (English)
Deep learning based unmixing methods have received great attention in recent years and achieve remarkable performance. These methods employ a data-driven approach to extract structure features from hyperspectral image, however, they tend to be less physical interpretable. Conventional unmixing methods are with much more interpretability, whereas they require manually designing regularization and choosing penalty parameters. To overcome these limitations, we propose a novel unmixing method by unrolling the plug-and-play unmixing algorithm to conduct the deep architecture. Our method integrates both inner and outer priors. The carefully designed unfolding deep architecture is used to learn the spectral and spatial information from the hyperspectral image, which we refer to as inner priors. Additionally, our approach incorporates deep denoisers that have been pretrained on a large volume of image data to leverage the outer priors. Secondly, we design a dynamic convolution to model the multiscale information. Different scales are fused using an attention module. Experimental results of both synthetic and real datasets demonstrate that our method outperforms compared methods.
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