Earticle

초록

영어

In recent years, l1 norm is usually considered as the regularization term in the field of sparse representation. However, the non-zero entries obtained by the l1 regularization term always neglect the correlations with each other. In fact, different relationships or structures among non-zero entries are necessary in many applications. K-support norm is firstly proposed in the field of sparse prediction. The most important property of the k-support norm is grouping feature of the largest entries in the obtained solution. In this paper, we present a new image processing model by introducing the k-support norm to image gradient domain. The proposed model can be applied to image denoising and edge detection simultaneously. Some examples demonstrate the effectiveness of the novel model and its improvements.

목차

Abstract
 1. Introduction
 2. K-support Norm and Related Notions
 3. Image Processing Model and Optimization
 4. Examples
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Junli Fan [ College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, P.R. China ]
• Xiaowei He [ College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, P.R. China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605