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Infrared and visible image fusion using salient decomposition based on a generative adversarial network

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Abstract

In order to address the fusion problem of infrared (IR) and visible images, this paper proposes a method using a local non-subsampled shearlet transform (LNSST) based on a generative adversarial network (GAN). We first decompose the source images into basic images and salient images by LNSST, then use two GANs fuse basic images and salient images. Lastly, we compose the fused basic images and salient images by inverse LNSST. We adopt public data sets to verify our method and by comparing with eight objective evaluation parameters obtained by 10 other methods. It is demonstrated that our method is able to achieve better performance than the state of the art on preserving both texture details and thermal information.

© 2021 Optical Society of America

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Data Availability

Data underlying the results presented in this paper are available in Refs. [38, 50].

38. A. Toet and E. M. Franken, “Perceptual evaluation of different image fusion schemes,” Displays 24, 25–37 (2003). [CrossRef]  

50. , “Video analytics dataset,” INO, accessed 2021, https://www.ino.ca/en/technologies/video-analytics-dataset/videos/.

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