论文标题
高光谱图像超分辨率，具有深度先验和降解模型反演
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
论文作者
论文摘要
为了克服高光谱成像系统的固有硬件局限性，基于融合的高光谱图像（HSI）超级分辨率正在吸引越来越多的关注。该技术旨在融合低分辨率（LR）HSI和常规高分辨率（HR）RGB图像，以获得HR HSI。最近，深度学习体系结构已被用来解决HSI超分辨率问题，并实现了出色的性能。但是，即使该模型具有明确的物理解释，他们也忽略了退化模型，并且可能有助于提高性能。我们通过提出一种方法来解决这个问题，该方法一方面利用目标函数的数据效率项中的线性降解模型，另一方面，它利用卷积神经网络的输出来设计频谱和空间渐变域中的先验常规器。实验表明，通过这种策略实现了绩效的提高。
To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention. This technique aims to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB image in order to obtain an HR HSI. Recently, deep learning architectures have been used to address the HSI super-resolution problem and have achieved remarkable performance. However, they ignore the degradation model even though this model has a clear physical interpretation and may contribute to improve the performance. We address this problem by proposing a method that, on the one hand, makes use of the linear degradation model in the data-fidelity term of the objective function and, on the other hand, utilizes the output of a convolutional neural network for designing a deep prior regularizer in spectral and spatial gradient domains. Experiments show the performance improvement achieved with this strategy.
