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

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 high-resolution (HR) conventional same scene obtain an HR HSI. In this work, we propose method that integrates physical model deep prior information. Specifically, novel, yet effective two-stream fusion network designed serve as regularizer for problem. This problem formulated optimization whose solution can be obtained by solving Sylvester equation. Furthermore, regularization parameter simultaneously estimated automatically adjust contribution learned reconstruct final Experimental results both simulated real data demonstrate superiority proposed over other state-of-the-art methods quantitative qualitative comparisons.