A variational pansharpening approach based on reproducible kernel Hilbert space and heaviside function
In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a panchromatic (PAN) image and a multispectral (MS) image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also design an iterative strategy to recover more image details. The final model is a convex one and solved by the designed alternating direction method of multipliers (ADMM) which guarantees the convergence of the proposed method. Experimental results on two real datasets corresponding to different sensors and different resolutions demonstrate the effectiveness of the proposed approach as compared with several state-of-the-art pansharpening approaches.