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论文类型： 期刊论文

发表时间： 2011-09-01

发表刊物： IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

收录刊物： Scopus、SCIE、EI

卷号： 5

期号： 5

页面范围： 1074-1082

ISSN号： 1932-4553

关键字： Features extraction; image fusion; joint sparse representation; K-SVD

摘要： In this paper, a novel joint sparse representation-based image fusion method is proposed. Since the sensors observe related phenomena, the source images are expected to possess common and innovation features. We use sparse coefficients as image features. The source image is represented with the common and innovation sparse coefficients by joint sparse representation. The sparse coefficients are consequently weighted by the mean absolute values of the innovation coefficients. Furthermore, since sparse representation has been significantly successful in the development of image denoising algorithms, our method can carry out image denoising and fusion simultaneously, while the images are corrupted by additive noise. Experiment results show that the performance of the proposed method is better than that of other methods in terms of several metrics, as well as in the visual quality.