The Image Sharpness Metric via Gaussian Mixture Modeling of the Quaternion Wavelet Transform Phase Coefficients with Applications
被引:0
作者:
Liu, Yipeng
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ Technol, Coll Informat Engn, Hangzhou, Zhejiang, Peoples R ChinaZhejiang Univ Technol, Coll Informat Engn, Hangzhou, Zhejiang, Peoples R China
Liu, Yipeng
[1
]
Du, Weiwei
论文数: 0引用数: 0
h-index: 0
机构:
Kyoto Inst Technol, Dept Informat Sci, Kyoto, JapanZhejiang Univ Technol, Coll Informat Engn, Hangzhou, Zhejiang, Peoples R China
Du, Weiwei
[2
]
机构:
[1] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou, Zhejiang, Peoples R China
[2] Kyoto Inst Technol, Dept Informat Sci, Kyoto, Japan
来源:
APPLIED COMPUTING & INFORMATION TECHNOLOGY
|
2016年
/
619卷
关键词:
TEXTURE CLASSIFICATION;
D O I:
10.1007/978-3-319-26396-0_13
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
In this paper, we make use of Gaussian mixture model (GMM) to describe the coefficients distribution of the quaternion wavelet transform (QWT). Derived from the parameters in GMM, the metric is proposed to find the relationship between the image blur degree and the distribution histograms of high frequencies coefficients. Also, the metric can be applied to smooth patch detection. Finally, experiments are conducted on natural images and the reasonable results indicate that the proposed metric can exhibit better performance than three common global sharpness measurements and satisfy the visual perception in the local smooth patch detection.