A new method of image feature extraction and denoising based on independent component analysis

被引：2
|
作者：
Yu, Ying [1 ]
Yang, Jian [2 ]
机构：
[1] Yunnan Univ, Sch Informat Sci & Technol, Kunming, Yunnan, Peoples R China
[2] Univ Yunnan Kunming, Sch Informat Sci & Technol, Yunnan Pr, Peoples R China
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-3 | 2006年
关键词：
sparse coding; feature extraction; independent component analysis; image denoising;
D O I：
10.1109/ROBIO.2006.340206
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to independent component analysis (ICA), and has some neurophysiological plausibility. In this paper, we show how to choose the optimal sparse coding basis for denoising and how to apply an improved shrinkage operation on the components of sparse coding so as to reduce noise. Compared to the method of wavelet shrinkage, our method has the important benefit that the features are estimated directly from data. We also show a new approach of sliding window to improve the efficiency of sparse code shrinkage for realtime processing.
引用
收藏
页码：380 / +
页数：2
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