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Color restoration of mural image based on double constrained convolutional neural network
期刊论文
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2020, 卷号: 48, 期号: 6, 页码: 6-12
作者:
Xu, Zhigang
;
Yin, Wenyu
;
Zhu, Xufeng
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2020/11/14
Color
Convolution
Convolutional neural networks
Discoloration
Markov processes
Multilayer neural networks
Restoration
Color features
Color restoration
Difference constraints
Global feature
Higher-order
Image-based
Local structure
Markov Random Fields
Deep High-order Supervised Hashing for Image Retrieval
会议论文
2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2018-01-01
作者:
Cheng, Jingdong
;
Sun, Qiule
;
Zhang, Jianxin
;
Wei, Xiaopeng
;
Zhang, Qiang
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  |  
浏览/下载:3/0
  |  
提交时间:2019/12/02
Benchmarking
Deep neural networks
Higher order statistics
Image enhancement
Neural networks
Pattern recognition, Cross correlations
Deep convolutional neural networks
Hashing functions
High order statistics
Multiple layers
Outer product
State of the art
State-of-the-art performance, Image retrieval
Lossless wavelet compression on medical image (EI CONFERENCE)
会议论文
4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China
Zhao X.
;
Wei J.
;
Zhai L.
;
Liu H.
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  |  
浏览/下载:28/0
  |  
提交时间:2013/03/25
An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image
thus facilitating accurate diagnosis
of course at the expense of higher bit rates
i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization
wavelet coding
neural networks
and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1
or even more)
they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image
but the achievable compression ratios are only of the order 2:1
up to 4:1. In our paper
we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time
we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance
so that all the low rate codes are included at the beginning of the bit stream. Typically
the encoding process stops when the target bit rate is met. Similarly
the decoder can interrupt the decoding process at any point in the bil stream
and still reconstruct the image. Therefore
a compression scheme generating an embedded code can start sending over the network the coarser version of the image first
and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.
A new blind source separation method based on fractional lower-order statistics
期刊论文
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2006, 卷号: 20, 页码: 213-223
作者:
Zha, DF
;
Qiu, TS
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浏览/下载:9/0
  |  
提交时间:2019/12/27
alpha stable distribution
blind source separation
independent component analysis
neural networks
second-order statistics
higher-order statistics
fractional lower-order statistics (FLOS)
non-Gaussian noise
Observing symmetry-breaking and chaos in the normal form network
期刊论文
NONLINEAR DYNAMICS, 2001, 卷号: 24, 期号: 3, 页码: 231-243
作者:
Chen, YH
;
Xu, JX
;
Fang, T
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  |  
浏览/下载:3/0
  |  
提交时间:2020/01/07
codimension-two bifurcation
symmetry-breaking
higher-order neural networks
second-order Poincare maps
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