DiriNet: A network to estimate the spatial and spectral degradation functions

TL;DR

This paper introduces DiriNet, a novel neural network that estimates spatial and spectral degradation functions in image fusion, using Dirichlet constraints to improve accuracy and robustness.

Contribution

It is the first to apply neural networks with Dirichlet regularization for estimating degradation functions in spectral image fusion.

Findings

Demonstrates superior performance over existing methods.

Effectively estimates degradation functions from image pairs.

Improves image fusion quality through accurate degradation modeling.

Abstract

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.