副教授 博士生导师 硕士生导师
性别: 男
毕业院校: 中国科学院大学
学位: 博士
所在单位: 信息与通信工程学院
联系方式: zhaowenda@dlut.edu.cn
电子邮箱: zhaowenda@dlut.edu.cn
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论文类型: 期刊论文
发表时间: 2018-04-01
发表刊物: IEEE TRANSACTIONS ON MULTIMEDIA
收录刊物: SCIE、EI、Scopus
卷号: 20
期号: 4
页面范围: 866-879
ISSN号: 1520-9210
关键字: Adaptive gain function; multiscale decomposition; multisensor image fusion and enhancement; spectral total variation (TV)
摘要: Most existing image fusion methods assume that at least one input image contains high-quality information at any place of an observed scene. Thus, these fusion methods will fail if every input image is degraded. To address this issue, this study proposes a novel fusion framework that integrates image fusion based on spectral total variation (TV) method and image enhancement. For spatially varying multiscale decompositions generated by the spectral TV framework, this study verifies that the decomposition components can be modeled efficiently by tailed astable-based random variable distribution (TRD) rather than the commonly used Gaussian distribution. Consequently, salience and match measures based on TRD are proposed to fuse each sub-band decomposition. The spatial intensity information is also adopted to fuse the remainder of the image decomposition components. A sub-band adaptive gain function family based on TV spectrum and space variation is constructed for fused multiscale decompositions to enhance fused image simultaneously. Finally, numerous experiments with various multisensor image pairs are conducted to evaluate the proposed method. Experimental results show that even if the input images are degraded, the fused image obtained by the proposed method achieves significant improvement in terms of edge details and contrast while extracting the main features of the input images, thereby achieving better performance compared with the state-of-the-art methods.