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长春光学精密机械与物... [5]
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期刊论文 [3]
会议论文 [1]
学位论文 [1]
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2018 [3]
2015 [1]
2011 [1]
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专题:长春光学精密机械与物理研究所
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Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain
期刊论文
Infrared Physics & Technology, 2018, 卷号: 92, 页码: 30-43
作者:
Cheng, B. Y.
;
Jin, L. X.
;
Li, G. N.
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浏览/下载:2/0
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提交时间:2019/09/17
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
A novel fusion framework of visible light and infrared images based on singular value decomposition and adaptive DUAL-PCNN in NSST domain
期刊论文
Infrared Physics & Technology, 2018, 卷号: 91, 页码: 153-163
作者:
Cheng, B. Y.
;
Jin, L. X.
;
Li, G. N.
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浏览/下载:2/0
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提交时间:2019/09/17
NSST
ADS-PCNN
Image fusion
Singular value decomposition
Local
structure information operator
Linking strength
shearlet transform
feature-extraction
nsct domain
algorithm
scheme
Instruments & Instrumentation
Optics
Physics
Infrared and visual image fusion using LNSST and an adaptive dual-channel PCNN with triple-linking strength
期刊论文
Neurocomputing, 2018, 卷号: 310, 页码: 135-147
作者:
Cheng, B. Y.
;
Jin, L. X.
;
Li, G. N.
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浏览/下载:11/0
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提交时间:2019/09/17
LNSST
ATD-PCNN
Image fusion
Singular value decomposition
Auxiliary
linking strength
Triple-linking strength
sparse representation
shearlet transform
multi-focus
feature-extraction
neural-network
domain
algorithm
decomposition
Computer Science
大视场多光谱相机图像拼接与融合技术研究
学位论文
博士: 中国科学院大学, 2015
作者:
李新娥
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浏览/下载:166/0
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提交时间:2015/11/30
图像融合
图像配准
图像拼接
颜色传递
Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE)
会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.
;
Wang M.-J.
;
Han G.-L.
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提交时间:2013/03/25
Being an efficient method of information fusion
image fusion has been used in many fields such as machine vision
medical diagnosis
military applications and remote sensing.In this paper
Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing
including segmentation
target recognition et al.
and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First
the two original images are decomposed by wavelet transform. Then
based on the PCNN
a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength
so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So
the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment
the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range
which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore
by this algorithm
the threshold adjusting constant is estimated by appointed iteration number. Furthermore
In order to sufficient reflect order of the firing time
the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved
each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules
the experiments upon Multi-focus image are done. Moreover
comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.
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