Antinoise Hyperspectral Image Fusion by Mining Tensor Low-Multilinear-Rank and Variational Properties

Abstract

Enhancing the spatial resolution of hyperspectral (HS) images by fusing with higher spatial resolution multispectral (MS) data is of significance for applications. However, due to the narrow bandwidth, HS images (HSIs) are vulnerable to various types of noise, such as Gaussian noise and stripes, which can severely affect the fusion performance. This paper focuses on antinoise HS and MS image fusion to enhance the spatial details and suppress the noise. By analysis of the intrinsic structure and noise properties, we formulate this problem as the minimization of an objective function. Under the optimization framework, small multilinear ranks in tensor are first used to identify the intrinsic structures of the clean HSI part. Then, considering the high spectral correlation, it is assumed that any bands can be represented by the combination of certain adjacent bands. The difference between one band and its corresponding combination can be used to preserve the spatio-spectral consistency and characterize the distribution of sparse noise (such as stripe noise), based on the variational properties along two directions. The alternating direction method of multipliers (ADMM) is applied to solve and accelerate the model optimization. Experiments with both simulated- and real-data demonstrate the effectiveness of the proposed model and its robustness to the noise, in terms of both qualitative and quantitative perspectives.