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Infrared and visible image fusion via joint convolutional sparse representation.
Journal of the Optical Society of America A ( IF 1.9 ) Pub Date : 2020-06-16 , DOI: 10.1364/josaa.388447
Minghui Wu , Yong Ma , Fan Fan , Xiaoguang Mei , Jun Huang

Recently, convolutional sparse representation (CSR) has improved the preservation of details of source images in the fusion results. This is mainly because the CSR has a global representation character that can improve spatial consistency in image representation. However, during image fusion processing, since the CSR expresses infrared and visible images separately, it ignores connections and differences between them. Further, CSR-based image fusion is not able to retain both strong intensity and clear details in the fusion results. In this paper, a novel fusion approach based on joint CSR is proposed. Specifically, we establish a joint form based on the CSR. The joint form is able to guarantee spatial consistency during image representation while obtaining distinct features, such as visible scene details and infrared target intensity. Experimental results illustrate that our fusion framework outperforms traditional fusion frameworks of sparse representation.

中文翻译:

通过联合卷积稀疏表示进行红外和可见图像融合。

最近,卷积稀疏表示(CSR)改进了融合结果中源图像细节的保存。这主要是因为CSR具有全局表示特征,可以提高图像表示中的空间一致性。但是,在图像融合处理期间,由于CSR分别表示红外图像和可见图像,因此它会忽略它们之间的连接和差异。此外,基于CSR的图像融合无法在融合结果中同时保留强强度和清晰细节。本文提出了一种基于联合CSR的融合方法。具体来说,我们基于CSR建立联合形式。联合形式能够确保图像表示期间的空间一致性,同时获得独特的功能,例如可见的场景细节和红外目标强度。
更新日期:2020-07-01
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