Earticle

초록

영어

There are two disadvantages in variational regularization based image restoration model. Firstly, the restored image is susceptible to noise because the diffusion coefficient depends on image gradient. Secondly, in the process of energy minimization, the selection of Lagrange multiplier λ which is used to balance the regular term and the fidelity term can directly affects the quality of the restored image. To solve the above problems, the multi-resolution feature of multi-scale wavelet is introduced into the energy minimization model and a wavelet based image restoration model is proposed. In the proposed model, Lagrange multiplier λ is replaced by an adaptive weighting function λj which is constructed by the image wavelet transform coefficients. Theoretical analysis and experiment results show that, comparing with other methods, the proposed model reduces iterations in the energy minimization process, overcomes the cartoon effects in variational model and pseudo-Gibbs effect in traditional wavelet threshold methods, and can well protect the detail features while denoising.

목차

Abstract
 1. Introduction
 2. The Total Variation Regularization Model
 3. Definition of Wavelet Transform Modulus and the Weighting Function
 4. Wavelet Domain Image Variational Restoration Model
 5. The Selection of Wavelet
 5. Results and Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Caixia Li [ School of Information and Electrical Engineering, Ludong University Yantai, China ]
• Chanjuan Liu [ School of Information and Electrical Engineering, Ludong University Yantai, China ] Corresponding author
• Hailin Zou [ School of Information and Electrical Engineering, Ludong University Yantai, China ]

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