Earticle

초록

영어

This paper models image deconvolution as an l2-l1 minimization problem, which is an approach taken by many state-of-the-art image deconvolution algorithms. We present a novel iterative algorithm based on the split Bregman method and the stationary second-degree method, which efficiently addresses the classic convex minimization problem. The split Bregman method, which has been proven to be very efficient for non-differentiable minimization problems, decomposes the equivalent constrained version of the l2-l1 deconvolution problem into a series of sub-problems. These sub-problems are then individually solved using appropriate methods to obtain their closed-form solutions. Unlike the majority of other similar deconvolution algorithms, we use a modified stationary second-degree method to solve the l2-l1 denoising sub-problem, prompted by some recent work on the improvement of the iterative thresholding method. The presented algorithm can be categorized as a split Bregman method, so convergence of the solution can be guaranteed. In our experiment, the presented algorithm and the algorithms in references [6] and [8] are used to restore Gaussian-blurry and uniform-blurry images. The experimental results show that the presented algorithm is effective and it outperforms other algorithms in comparison.

목차

Abstract
 1. Introduction
 2. Review of SBM
 3. Presented Deconvolution Algorithm
 4. Experimental Results
 5. Conclusions
 References

키워드

저자

• Su Xiao [ School of Computer Science and Technology, Huaibei Normal University Huaibei, 235000, 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