Infrared dual-band image fusion with simplified pulse coupled neural network and visual saliency map in nonsubsampled shearlet transform domain

Xiyuan Su; Changqing Cao; Xiaodong Zeng; Zhejun Feng; Ningjin Na; Wenrui Zhang; Yutao Liu; Ting Wang; Xu Yan

doi:10.1117/12.2543275

20 December 2019 Infrared dual-band image fusion with simplified pulse coupled neural network and visual saliency map in nonsubsampled shearlet transform domain

Xiyuan Su, Changqing Cao, Xiaodong Zeng, Zhejun Feng, Ningjin Na, Wenrui Zhang, Yutao Liu, Ting Wang, Xu Yan

Author Affiliations +

Proceedings Volume 11209, Eleventh International Conference on Information Optics and Photonics (CIOP 2019); 1120912 (2019) https://doi.org/10.1117/12.2543275
Event: Eleventh International Conference on Information Optics and Photonics (CIOP 2019), 2019, Xi'an, China

Abstract

As an effective way to integrate complementary information in multisensor detection system, image fusion technology has been widely used in robotic vision, medical diagnosis and safety monitoring. At the same time, the dual band infrared detection system has been widely used in the field of guidance and detection.Because dual-band/multi-band infrared detection has the characteristics of wide detection range and multi-target radiation information. Therefore, there is an urgent need of a fusion of the dual-bands infrared images. In order to obtain better image quality, infrared dual-frequency image fusion technology is used to synthesize different radiation information of target and background.In this paper, a new infrared dual-band image fusion with simplified pulse coupled neural network(PCNN) and visual saliency map(VSM) Framework in nonsubampled shearlet domain (NSST) is proposed. In the proposed method, first, the sours images are decomposed into base parts and multiscale and multidirection representations in NSST domain. Then，base parts are fused by VSM fusion approach. For the high-frequency bands are fused by a Simplified pulse coupled neural network model. Finally, the final image is reconstructed by inverse NSST. As a result, the fused image details will be presented more naturally, which is more suitable for human visual perception. The experimental results demonstrate that evaluation quality of the fused images is improved by comparing three objective evaluation factors with three popular fusion methods.This technology is of great significance to the development of image field.

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Xiyuan Su, Changqing Cao, Xiaodong Zeng, Zhejun Feng, Ningjin Na, Wenrui Zhang, Yutao Liu, Ting Wang, and Xu Yan "Infrared dual-band image fusion with simplified pulse coupled neural network and visual saliency map in nonsubsampled shearlet transform domain", Proc. SPIE 11209, Eleventh International Conference on Information Optics and Photonics (CIOP 2019), 1120912 (20 December 2019); https://doi.org/10.1117/12.2543275

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KEYWORDS

Image fusion

Infrared imaging

Infrared radiation

Neural networks

Visualization

Medical imaging

Image filtering

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