A Variational Pan-Sharpening With Local Gradient Constraints
Pan-sharpening aims at fusing spectral and spatial information, which are respectively contained in the multispectral (MS) image and panchromatic (PAN) image, to produce a high resolution multi-spectral (HRMS) image. In this paper, a new variational model based on a local gradient constraint for pan-sharpening is proposed. Different with previous methods that only use global constraints to preserve spatial information, we first consider gradient difference of PAN and HRMS images in different local patches and bands. Then a more accurate spatial preservation based on local gradient constraints is incorporated into the objective to fully utilize spatial information contained in the PAN image. The objective is formulated as a convex optimization problem which minimizes two leastsquares terms and thus very simple and easy to implement. A fast algorithm is also designed to improve efficiency. Experiments show that our method outperforms previous variational algorithms and achieves better generalization than recent deep learning methods.
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