DiriNet: An Estimation Network for Spectral Response Function and Point Spread Function

Hyper- and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images, which always depends on the spectral response function and the point spread function. However, few works have been payed on the estimation of the two degradation functions. To learn the two functions from image pairs to be fused, we propose a Dirichlet network, where both functions are properly constrained. Specifically, the spatial response function is constrained with positivity, while the Dirichlet distribution along with a total variation is imposed on the point spread function. To the best of our knowledge, the neural network and the Dirichlet regularization are exclusively investigated, for the first time, to estimate the degradation functions. Both image degradation and fusion experiments demonstrate the effectiveness and superiority of the proposed Dirichlet network.