中国科学院机构知识库网格系统: 检索

Multi-focus image fusion dataset and algorithm test in real environment 期刊论文 OAI收割
FRONTIERS IN NEUROROBOTICS, 2022, 卷号: 16, 页码: 8
作者: Liu, Shuaiqi; Peng, Weijian; Jiang, Wenjing; Yang, Yang; Zhao, Jie

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收藏 | 浏览/下载：35/0 | 提交时间：2022/12/27

image fusion multi-focus image fusion dataset image preprocessing multi-focus image fusion algorithm test real environment

Corrigendum: Investigations on Average Fluorescence Lifetimes for Visualizing Multi-Exponential Decays (vol 8, 576862, 2020) 期刊论文 OAI收割
FRONTIERS IN PHYSICS, 2021, 卷号: 8
作者: Li, Yahui; Natakorn, Sapermsap; Chen, Yu; Safar, Mohammed; Cunningham, Margaret

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收藏 | 浏览/下载：66/0 | 提交时间：2021/03/26

fluorescence lifetime imaging lifetime determination algorithm average lifetimes multi-exponential decays lifetime image visualization FRET-fluorescence resonance energy transfer

Investigations on Average Fluorescence Lifetimes for Visualizing Multi-Exponential Decays 期刊论文 OAI收割
FRONTIERS IN PHYSICS, 2020, 卷号: 8
作者: Li, Yahui; Natakorn, Sapermsap; Chen, Yu; Safar, Mohammed; Cunningham, Margaret

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收藏 | 浏览/下载：94/0 | 提交时间：2020/11/23

fluorescence lifetime imaging lifetime determination algorithm average lifetimes multi-exponential decays lifetime image visualization FRET— fluorescence resonance energy transfer

Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion 会议论文 OAI收割
Osaka, Japan, 2020-05-19
作者: Wang, Huanting; Qu, Bo; Lu, Xiaoqiang; Chen, Yaxiong

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收藏 | 浏览/下载：27/0 | 提交时间：2020/08/21

Multi-channel feature fusion Unsupervised hashing algorithm VAE Image retrieval

Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows 期刊论文 OAI收割
COMPUTERS & GEOSCIENCES, 2019, 卷号: 123, 页码: 95-102
作者: Pan, Shulin; Qin, Ziyu; Lan, Haiqiang; Badal, Jose

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收藏 | 浏览/下载：51/0 | 提交时间：2019/04/18

First-arrival picking Image connectivity algorithm Multi time windows Interpolation

Infrared and visual image fusion using LNSST and an adaptive dual-channel PCNN with triple-linking strength 期刊论文 OAI收割
Neurocomputing, 2018, 卷号: 310, 页码: 135-147
作者: Cheng, B. Y.; Jin, L. X.; Li, G. N.

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收藏 | 浏览/下载：32/0 | 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

A Real-Time Image Processing Method for Multi-Beam Forward-Looking Sonar of AUV 会议论文 OAI收割
2014 International Conference on Mechatronics Engineering and Modern Technologies in Industrial Engineering (MEMTIE 2014), Changsha, Hunan, China, October 25-26, 2014
作者: Gao L(高雷); Xu HL(徐红丽)

收藏 | 浏览/下载：19/0 | 提交时间：2015/12/13

AUV Fuzzy Clustering Algorithm Multi-Beam Sonar Sonar Image Processing

A line mapping based automatic registration algorithm of infrared and visible images 会议论文 OAI收割
5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Infrared Imaging and Applications, Beijing, June 25-27, 2013
作者: Ai R(艾锐); Shi ZL(史泽林); Xu DJ(徐德江); Zhang CS(张程硕)

收藏 | 浏览/下载：37/0 | 提交时间：2013/12/26

There exist complex gray mapping relationships among infrared and visible images because of the different imaging mechanisms. The difficulty of infrared and visible image registration is to find a reasonable similarity definition. In this paper, we develop a novel image similarity called implicit linesegment similarity(ILS) and a registration algorithm of infrared and visible images based on ILS. Essentially, the algorithm achieves image registration by aligning the corresponding line segment features in two images. First, we extract line segment features and record their coordinate positions in one of the images, and map these line segments into the second image based on the geometric transformation model. Then we iteratively maximize the degree of similarity between the line segment features and correspondence regions in the second image to obtain the model parameters. The advantage of doing this is no need directly measuring the gray similarity between the two images. We adopt a multi-resolution analysis method to calculate the model parameters from coarse to fine on Gaussian scale space. The geometric transformation parameters are finally obtained by the improved Powell algorithm. Comparative experiments demonstrate that the proposed algorithm can effectively achieve the automatic registration for infrared and visible images, and under considerable accuracy it makes a more significant improvement on computational efficiency and anti-noise ability than previously proposed algorithms.

Precise motion compensation based on weighted sub-pixel image matching (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Liang H. G.

收藏 | 浏览/下载：25/0 | 提交时间：2013/03/25

This paper proposed a sub-pixel image correlation algorithm that can get more Precise result its principle is apply the distribute of relativity peak to get weighted multi-pixel comprehensive of location. Image correlation be as to calculates the greyscale relativity of image template and matching image the relativity of correspond location where match best with template will be most high and in its neighbour range the relativity will be still keep high too. We used these pixel in this local area of calculated match point to get sub-pixel accuracy the relativity of every pixel be used as its weight for participate the sub-pixel calculation. The sub-pixel location is more accuracy than the integer one we applied this method to perform background compensation in processing the target detecting for video image sequence. At the end of this paper some experiment data be proposed it proved this sub-pixel image correlation can obtain better result. 2011 SPIE.

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
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.

收藏 | 浏览/下载：78/0 | 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.