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Optical Engineering

Simultaneous spatial-temporal image fusion using Kalman filtered compressed sensing
Author(s): Han Pan; Zhongliang Jing; Rongli Liu; Bo Jin
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Paper Abstract

Image fusion is a process to combine multiple frames of the same scene into one image. The popular image fusion methods mainly concentrate on static image fusion and lack spatial-temporal adaptability. The conventional multi-resolution image fusion algorithms have not fully exploited the temporal information. To resolve this problem, we present a novel dynamic image fusion algorithm based on Kalman filtered compressed sensing. The fusion procedure characterized by estimation fusion is completed in state space. A parametric fusion model is proposed to learn and combine spatial and temporal information simultaneously. The experiments on the ground-truth data sets show that the proposed fusion algorithm offers a considerable improvement on the dynamic fusion performance and rivals the traditional multi-resolution-based fusion methods.

Paper Details

Date Published: 22 May 2012
PDF: 14 pages
Opt. Eng. 51(5) 057005 doi: 10.1117/1.OE.51.5.057005
Published in: Optical Engineering Volume 51, Issue 5
Show Author Affiliations
Han Pan, Shanghai Jiao Tong Univ. (China)
Zhongliang Jing, Shanghai Jiao Tong Univ. (China)
Rongli Liu, Shanghai Jiao Tong Univ. (China)
Bo Jin, Shanghai Jiao Tong Univ. (China)

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