PAN-SHARPENINGWITH MULTI-SCALEWAVELET DICTIONARY

In satellite image processing, pan-sharpening is the fusion process in which a low resolution (LR) multi-spectral (MS) image is sharp-ened using the corresponding high resolution (HR) panchromatic (Pan) image to obtain a HR MS image. In this paper we propose a novel pan-sharpening method which combines the ideas of clas-sical wavelet-based pan-sharpening with recently developed dictio-nary learning (DL) methods. The HR MS image is generated using wavelet-based pan-sharpening, regulated by promoting sparsity with respect to a dictionary. The dictionary is obtained using DL on the multi-scale wavelet tree vectors of the image to be pan-sharpened. A significant advantage of our approach compared to most DL-based approaches is that it does not require a large database of images on which to train the dictionary. Experiments on degraded satellite im-ages demonstrate that our method significantly reduces color distor-tions and wavelet artifacts compared to the state of the art. 1