Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain

doi:10.1016/j.infrared.2018.04.017

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain
	Cheng, B. Y.; Jin, L. X.; Li, G. N.
刊名	Infrared Physics & Technology
	2018
卷号	92 页码:30-43
关键词	LNSST Image fusion Improved dual-channel PCNN Frequency-tuned saliency detection sparse representation contourlet transform shearlet transform feature-extraction algorithm decomposition Instruments & Instrumentation Optics Physics
ISSN号	1350-4495
DOI	10.1016/j.infrared.2018.04.017
英文摘要	This paper presents an adaptive fusion framework of infrared and visual images using saliency detection and an improved dual-channel pulse-coupled neural network (ID-PCNN) in the local non-subsampled shearlet transform (LNSST) domain. The first step is to use the LNSST, an upgrade of the non-subsampled shearlet transform, for multi-scale analysis to separate the source images into low-pass and high-pass sub-images. The final fusion effect is determined by the fusion rule of the low-pass component. Thus, an improved algorithm based on frequency-tuned saliency extraction is adopted to guide the adaptive weighted fusion of the low-pass sub-image. An ID-PCNN model is used as the fusion rule for high-pass sub-images. A sum of directional gradients acts as the linking strength to characterize the texture details of an image. A modified spatial frequency that reflects the gradient features of images is used to motivate neurons. A series of images from diverse scenes is used for fusion experiments. Fusion results are evaluated subjectively and objectively. The results show that our algorithm exhibits superior fusion performance and is more effective than typical fusion techniques. (C) 2018 Elsevier B.V. All rights reserved.
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/60891]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Cheng, B. Y.,Jin, L. X.,Li, G. N.. Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain[J]. Infrared Physics & Technology,2018,92:30-43.
APA	Cheng, B. Y.,Jin, L. X.,&Li, G. N..(2018).Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain.Infrared Physics & Technology,92,30-43.
MLA	Cheng, B. Y.,et al."Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain".Infrared Physics & Technology 92(2018):30-43.