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Optical Engineering • Open Access • new

Pansharpening via sparse regression
Author(s): Songze Tang; Liang Xiao; Pengfei Liu; Lili Huang; Nan Zhou; Yang Xu

Paper Abstract

Pansharpening is an effective way to enhance the spatial resolution of a multispectral (MS) image by fusing it with a provided panchromatic image. Instead of restricting the coding coefficients of low-resolution (LR) and high-resolution (HR) images to be equal, we propose a pansharpening approach via sparse regression in which the relationship between sparse coefficients of HR and LR MS images is modeled by ridge regression and elastic-net regression simultaneously learning the corresponding dictionaries. The compact dictionaries are learned based on the sampled patch pairs from the high- and low-resolution images, which can greatly characterize the structural information of the LR MS and HR MS images. Later, taking the complex relationship between the coding coefficients of LR MS and HR MS images into account, the ridge regression is used to characterize the relationship of intrapatches. The elastic-net regression is employed to describe the relationship of interpatches. Thus, the HR MS image can be almost identically reconstructed by multiplying the HR dictionary and the calculated sparse coefficient vector with the learned regression relationship. The simulated and real experimental results illustrate that the proposed method outperforms several well-known methods, both quantitatively and perceptually.

Paper Details

Date Published: 28 September 2017
PDF: 13 pages
Opt. Eng. 56(9) 093105 doi: 10.1117/1.OE.56.9.093105
Published in: Optical Engineering Volume 56, Issue 9
Show Author Affiliations
Songze Tang, Nanjing Forest Police College (China)
Liang Xiao, Nanjing Univ. of Science and Technology (China)
Pengfei Liu, Nanjing Univ. of Posts and Telecommunications (China)
Lili Huang, Guangxi Univ. of Science and Technology (China)
Nan Zhou, Nanjing Forest Police College (China)
Yang Xu, Nanjing Univ. of Science and Technology (China)

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