Variational Pansharpening Based on Coefficient Estimation With Nonlocal Regression

Pansharpening (which stands for panchromatic (PAN) sharpening) involves the fusion between a multispectral (MS) image with a higher spectral content than a fine spatial resolution PAN image to generate a high spatial resolution MS (HRMS) image. A widely used concept is the construction of the relationship between PAN and HRMS images by designing pixel-based coefficients. Previous pixel-based methods compute the coefficients pixel-by-pixel while suffering from inaccuracies in some areas leading to spatial distortion. However, we found that the coefficients inherit the spatial properties of the HRMS image, e.g., the local smoothness and nonlocal self-similarity, and the spatial correlation between the coefficients and the HRMS image can increase the accuracy of the estimation process. In this article, we propose a novel spatial fidelity with nonlocal regression (SFNLR) to describe the relationship between PAN and HRMS images. Unlike from the pixel-based perspective, the SFNLR can jointly use the local smoothness and nonlocal self-similarity of the coefficients for preserving spatial information. Besides, the SFNLR is integrated with a widely used spectral fidelity to formulate a new variational model for the pansharpening problem. An effective algorithm based on the alternating direction method of multiplier (ADMM) framework is designed to solve the proposed model. Qualitative and quantitative assessments on reduced and full resolution datasets from different satellites demonstrate that the proposed approach outperforms several state-of-the-art methods. The code is available at: https://github.com/Jin-liangXiao/SFNLR.