A Principle Design of Registration-Fusion Consistency: Toward Interpretable Deep Unregistered Hyperspectral Image Fusion

For hyperspectral image (HSI) and multispectral image (MSI) fusion, it is often overlooked that multisource images acquired under different imaging conditions are difficult to be perfectly registered. Although some works attempt to fuse unregistered images, two thorny challenges remain. One is that registration and fusion are usually modeled as two independent tasks, and there is no yet a unified physical model to tightly couple them. Another is that deep learning (DL)-based methods may lack sufficient interpretability and generalization. In response to the above challenges, we propose an unregistered HSI fusion framework energized by a unified model of registration and fusion. First, a novel registration-fusion consistency physical perception model (RFCM) is designed, which uniformly models the image registration and fusion problem to greatly reduce the sensitivity of fusion performance to registration accuracy. Then, an HSI fusion framework (MoE-PNP) is proposed to learn the knowledge reasoning process for solving RFCM. Each basic module of MoE-PNP one-to-one corresponds to the operation in the optimization algorithm of RFCM, which can ensure clear interpretability of the network. Moreover, MoE-PNP captures the general fusion principle for different unregistered images and therefore has good generalization. Extensive experiments demonstrate that MoE-PNP achieves state-of-the-art performance for unregistered HSI and MSI fusion. The code is available at https://github.com/Jiahuiqu/MoE-PNP.