论文标题
高光谱图像超分辨率通过深度正规化和参数估计
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
论文作者
论文摘要
高光谱图像（HSI）超分辨率通常用于克服空间分辨率上现有高光谱成像系统的硬件限制。它融合了低分辨率（LR）HSI和同一场景的高分辨率（HR）常规图像以获得HR HSI。在这项工作中，我们提出了一种整合物理模型和深度先前信息的方法。具体而言，一种新颖而有效的两流融合网络旨在用作融合问题的{正则器}。这个融合问题被提出为优化问题，可以通过求解sylvester方程来获得解决方案。此外，同时估计正则化参数可以自动调整物理模型的贡献，并在重建最终的HR HSI之前学到了{}。 {模拟和真实数据}的实验结果证明了所提出的方法比其他最先进方法在定量和定性比较上的优越性。
Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-resolution (LR) HSI and a high-resolution (HR) conventional image of the same scene to obtain an HR HSI. In this work, we propose a method that integrates a physical model and deep prior information. Specifically, a novel, yet effective two-stream fusion network is designed to serve as a {regularizer} for the fusion problem. This fusion problem is formulated as an optimization problem whose solution can be obtained by solving a Sylvester equation. Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and {the} learned prior to reconstruct the final HR HSI. Experimental results on {both simulated and real data} demonstrate the superiority of the proposed method over other state-of-the-art methods on both quantitative and qualitative comparisons.
