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Proceedings Paper

A new variational fusion method for remote sensing images based on sparse representation
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Paper Abstract

The variational method was popular and effective for image fusion in recent years, and it could constraint the geometric and gradient structures in original images according to the variational fusion equation with preserving information terms. The sparse representation was widely used for image decomposition and fused image reconstruction by combining sparse coefficients of multi-source images. Therefore, a new variational image fusion method was proposed by adding an approximated term with sparse representation model. What’s more, the sparse representation fusion was improved with the new coefficient fusion rule, and then was joined within the variational fusion frame. The proposed variational fusion method could approximate multiband images and preserve details. The fusion experiments were performed on GF-2 remote sensing images, and compared with original unimproved methods and some usual fusion methods. The results showed that the new proposed method is better than tested methods in accordance with objective evaluations and subjective visual effects.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064X (9 August 2018); doi: 10.1117/12.2502998
Show Author Affiliations
Jiuxing Zhang, Technology and Engineering Ctr. for Space Utilization (China)
Wei Zhang, Technology and Engineering Ctr. for Space Utilization (China)
Pei Han, Technology and Engineering Ctr for Space Utilization (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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