论文标题
DIRINET：估计空间和光谱降解功能的网络
DiriNet: A network to estimate the spatial and spectral degradation functions
论文作者
论文摘要
空间和光谱降解函数对超光谱图像融合至关重要。但是，关于降解功能的估计，几乎没有支付工作。为了学习空间响应函数和从要融合的图像对中的点扩展函数，我们提出了一个Dirichlet网络，其中两个函数都得到了适当的约束。具体而言，空间响应函数受阳性的限制，而dirichlet分布以及总变化对点扩散函数施加。据我们所知，首次对神经NetWrok和Dirichlet正则化进行了首次研究，以估计降解功能。图像降解和融合实验都证明了所提出的Dirichlet网络的有效性和优势。
The spatial and spectral degradation functions are critical to hyper- and multi-spectral image fusion. However, few work has been payed on the estimation of the degradation functions. To learn the spatial response function and the point spread function from the 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 netwrok 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.
