Refinement method for compressive hyperspectral data cubes based on self-fusion

Abstract

Compressive hyperspectral images often suffer from various noises and artifacts, which severely degrade the imaging quality and limit subsequent applications. In this paper, we present a refinement method for compressive hyperspectral data cubes based on self-fusion of the raw data cubes, which can effectively reduce various noises and improve the spatial and spectral details of the data cubes. To verify the universality, flexibility, and extensibility of the self-fusion refinement (SFR) method, a series of specific simulations and practical experiments were conducted, and SFR processing was performed through different fusion algorithms. The visual and quantitative assessments of the results demonstrate that, in terms of noise reduction and spatial–spectral detail restoration, the SFR method generally is much better than other typical denoising methods for hyperspectral data cubes. The results also indicate that the denoising effects of SFR greatly depend on the fusion algorithm used, and SFR implemented by joint bilateral filtering (JBF) performs better than SRF by guided filtering (GF) or a Markov random field (MRF). The proposed SFR method can significantly improve the quality of a compressive hyperspectral data cube in terms of noise reduction, artifact removal, and spatial and spectral detail improvement, which will further benefit subsequent hyperspectral applications.