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arXiv · 2009.04237

Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation

Xiuheng Wang·Jie Chen·Qi Wei·Cédric Richard
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Abstract

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.

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Xiuheng Wang, Jie Chen, Qi Wei, Cédric Richard. 2021-04-25. Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation. https://arxiv.org/abs/2009.04237

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