A compressed sensing-based pan-sharpening using joint data fidelity and blind blurring kernel estimation

Abstract

Pan-sharpening is an approach that fuse low resolution multi-spectral (LRMS) images with a high spatial detail of panchromatic (PAN) image to obtain the high resolution multispectral (HRMS) images. In this paper, we present a compressed sensing-based pan-sharpening method that include joint data fidelity and blind blurring kernel estimation. The joint data fidelity contain following three fidelity terms: (1) the LRMS images could be the decimated form of the HRMS images by convolving a blurring kernel, (2) the gradient of HRMS images in the spectrum direction could be proximity to those of the LRMS images, (3) the high frequency part of linear combination of HRMS image bands is approximate to the corresponding parts of the PAN image. Different from other methods which simply apply average blurring kernel for pan-sharpening, a blind deconvolution algorithm is introduced to estimate the blurring kernel from different satellites respectively. We also include a novel anisotropic total variation (TV) prior term to better reconstruct the image edges. The alternating direction method of multipliers (ADMM) is used to solve the proposed model efficiently. Finally, a Pleiades satellite image is employed to demonstrate that the proposed method achieve effective and efficient results simultaneously compared with other existing methods.