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	Exposure fusion via sparse representation and shiftable complex directional pyramid transform 期刊论文 OAI收割 MULTIMEDIA TOOLS AND APPLICATIONS, 2017, 卷号: 76, 期号: 14, 页码: 15755-15775 作者: Wang, Jinhua; Wang, Weiqiang; Li, Bing; Xu, Guangmei; Zhang, Ruizhe \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2017/09/12 Exposure Fusion Pdtdfb Sparse Representation Fusion Rule
	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：84/0 \| 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.
	Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis 期刊论文 OAI收割 EXPERT SYSTEMS WITH APPLICATIONS, 2011, 卷号: 38, 期号: 10, 页码: 9,12151-12159 Chen, ZY; Li, JP; Wei, LW; Xu, WX; Shi, Y 收藏 \| 浏览/下载：32/0 \| 提交时间：2012/11/12 Support vector machine Multiple-kernel learning Feature selection Data fusion Decision rule Associated rule Subclass discovery Gene expression
	A new image fusion algorithm based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割 2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China 作者: He X.; Zhang Y.; Zhang L.-G.; Zhang L.-G. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 A new image fusion algorithm based on lifting wavelet transform is presented in this paper. The source images are decomposed using lifting wavelet transform respectively. Aiming at the coefficients of low frequency and high frequency this algorithm choose a different rule to fuse the image. To the low frequency the spatial frequency based on the neighborhood add consistency check is elected as the fusion guide. And the absolute maximum based on detail coefficients is selected as the guide to the high frequency. After that the fused image is obtained by using inverse lifting wavelet transform. Taking the ratio space frequency error and the mean gradient as criterions experimental results demonstrate that the algorithm is very effective. 2010 IEEE.
	Multiwavelet based multispectral image fusion for corona detection (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang X.; Yang H.-J.; Sui Y.-X.; Yan F.; Yan F. 收藏 \| 浏览/下载：34/0 \| 提交时间：2013/03/25 Image fusion refers to the integration of complementary information provided by various sensors such that the new images are more useful for human or machine perception. Multiwavelet transform has simultaneous orthogonality symmetry compact support and vanishing moment which are not possible with scalar wavelet transform. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. In this paper a new image fusion algorithm based on discrete multiwavelet transform (DMWT) to fuse the dual-spectral images generated from the corona detection system is presented. The dual-spectrum detection system is used to detect the corona and indicate its exact location. The system combines a solar-blind UV ICCD with a visible camera where the UV image is useful for detecting UV emission from corona and the visible image shows the position of the corona. The developed fusion algorithm is proposed considering the feature of the UV and visible images adequately. The source images are performed at the pixel level. First a decomposition step is taken with the DMWT. After the decomposition step a pyramid for each source image in each level can be obtained. Then an optimized coefficient fusion rule consisting of activity level measurement coefficient combining and consistency verification is used to acquire the fused coefficients. This process reduces the impulse noise of UV image. Finally a new fused image is obtained by reconstructing the fused coefficients using inverse DMWT. This image fusion algorithm has been applied to process the multispectral UV/visible images. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach.