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浏览/检索结果: 共6条，第1-6条 帮助

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OptiFX: Automatic Optimization for Convolutional Neural Networks with Aggressive Operator Fusion on GPUs 期刊论文  OAI收割
ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, 2025, 卷号: 22, 期号: 2, 页码: 27
作者:  Wang, Xueying;  Li, Shigang;  Qian, Hao;  Luo, Fan;  Hao, Zhaoyang
  |  收藏  |  浏览/下载：3/0  |  提交时间：2025/12/03
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  OAI收割
Infrared Physics & Technology, 2018, 卷号: 91, 页码: 153-163
作者:  Cheng, B. Y.;  Jin, L. X.;  Li, G. N.
  |  收藏  |  浏览/下载：68/0  |  提交时间：2019/09/17
Image Fusion Based on the Delta(-1) - TV0 Energy Function 期刊论文  OAI收割
ENTROPY, 2014, 卷号: 16, 期号: 11, 页码: 6099-6115
作者:  Xie, Qiwei;  Ma, Chao;  Guo, Chunzhao;  John, Vijay;  Mita, Seiichi
收藏  |  浏览/下载：58/0  |  提交时间：2015/08/12
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：167/0  |  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.  
T-Q relation and exact solution for the XYZ chain with general non-diagonal boundary terms 期刊论文  OAI收割
NUCLEAR PHYSICS B, 2006, 卷号: 744, 页码: 312-329
作者:  Yang, Wen-Li;  Zhang, Yao-Zhong
  |  收藏  |  浏览/下载：35/0  |  提交时间：2018/05/31
Q-operator and T-Q relation from the fusion hierarchy 期刊论文  OAI收割
PHYSICS LETTERS B, 2006, 卷号: 633, 页码: 664-670
作者:  Yang, WL;  Nepomechie, RI;  Zhang, YZ
  |  收藏  |  浏览/下载：29/0  |  提交时间：2018/05/31