Abstract: Regarding existing classical matrix decomposition models,they may lead to the loss of three-dimensional data structure information,especially when affected by noise pollution,resulting in a significant decrease in the quality of reconstructed images,this paper proposes a method for hyperspectral and multispectral fusion that combines subspace smoothing with graph regularization.This approach aims to achieve hyperspectral image super-resolution reconstruction by utilizing manifold structures and local smoothing characteristics,while preserving cube structure features.Firstly,the local self-similarity between spatial subspace and spectral subspace is used to construct spatial and spectral maps by tensor ring factors to mine spatial spectral manifold structure to improve the quality of reconstructed images.Secondly,the subspace smoothing regularization is introduced to promote the segmentation smoothing of the subspace of the target image.Finally,an efficient proximal alternating minimization algorithm is designed to solve the proposed model.Experiments on three commonly used experimental data sets show that the proposed model can improve the spatial details and structure while suppressing the noise to a certain extent.

Key words: Hyperspectral imaging, Hyperspectral and multispectral image fusion, Tensor ring decomposition, Graph regularization, Subspace smoothing regularization