A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain

Abstract

In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the spectral information of the MS image. I is used as the initial α channel to estimate the foreground color F and the background color B. Depending on the NSST, the PAN image and I are decomposed into one low-frequency component and several high-frequency components, respectively. Fusion rules are designed corresponding to the characteristics of the low-frequency and high-frequency components. A β-divergence NMF method based on the Alternating Direction Method of Multipliers (ADMM) is used to fuse the low frequency components. A WLCM-based rule is used to fuse the high-frequency components. The fused components are inverted by NSST inverse transformation, and the obtained image is used as the final α channel. Finally, the final fused image is reconstructed according to the foreground color F, background color B, and the final α channel. The experimental results demonstrate that the proposed method achieves superior performance in both subjective visual effects and objective evaluation, and effectively preserves spectral information while improving spatial resolution.