论文标题
混合网络：低排放的深图像先验受到混合模型的启发，用于光谱图像恢复
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
论文作者
论文摘要
本文提出了一个非DATA驱动的深神经网络，用于光谱图像恢复问题，例如denoising，单光谱图像超分辨率和压缩光谱成像重建。与以前的方法不同，所提出的方法称为混合网络，隐含地通过网络学习了先前的信息。混合网络由深层生成模型组成，该模型的层是受线性和非线性低级别混合模型的启发，其中恢复的图像由线性和非线性分解之间的加权总和组成。混合网络还提供了一个低级分解，该分解解释为光谱图像的丰度和末端成员，有助于实现遥感任务而无需运行其他例程。实验表明，Mixturenet有效性在恢复质量方面的最新方法优于架构解释性的优势。
This paper proposes a non-data-driven deep neural network for spectral image recovery problems such as denoising, single hyperspectral image super-resolution, and compressive spectral imaging reconstruction. Unlike previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns the prior information through the network. Mixture-Net consists of a deep generative model whose layers are inspired by the linear and non-linear low-rank mixture models, where the recovered image is composed of a weighted sum between the linear and non-linear decomposition. Mixture-Net also provides a low-rank decomposition interpreted as the spectral image abundances and endmembers, helpful in achieving remote sensing tasks without running additional routines. The experiments show the MixtureNet effectiveness outperforming state-of-the-art methods in recovery quality with the advantage of architecture interpretability.
