摘要
为减少阶梯效应,同时更好地利用图像本身的信息,提出一种结合分数阶全变差(FOTV)和非局部全变差(NLTV)模型的非盲去模糊图像重建方法。分别用FOTV和NLTV约束由全局梯度提取法(GGES)分解而成的平滑区和纹理区,建立图像非盲去模糊的正则化模型,分别采用交替方向乘子法(ADMM)和分裂Bregman操作符(BOS)算法求解两个子问题。充分的实验结果表明,该模型减少了平滑区的阶梯效应,更好地恢复了图像的纹理细节,验证了该模型的可行性和算法的有效性。
To reduce the staircase artifacts and use more information of the blurred image itself,an image reconstruction method of non-blind deblurring by combining the fractional order total variation(FOTV)and the non-local total variation(NLTV)models was proposed.The FOTV and NLTV regularization were used to constrain the flat and texture regions decomposed through global gradient extraction scheme(GGES),respectively,and a regularization optimization model of non-blind deblurring was constructed.Alternating direction method of multipliers(ADMM)was used to solve the FOTV sub-problems,while the algorithm of Bregman operator splitting(BOS)was used to solve NLTV sub-problems.Lots of experimental results and data demonstrate that the proposed method contains the advantages of FOTV and NLTV,it can not only reduce the staircase artifacts in flat regions,but also reconstruct more texture details of the blurred images.The feasibility and efficiency of the proposed method and corresponding algorithm are verified.
作者
向雨晴
杨晓梅
XIANG Yu-qing,YANG Xiao-mei(School of Electrical Engineering and Information,Sichuan University,Chengdu 610065,Chin)
出处
《计算机工程与设计》
北大核心
2018年第7期2002-2007,共6页
Computer Engineering and Design
关键词
非盲去模糊
分数阶全变差
非局部全变差
交替方向乘子法
正则化
non-blind deblurring
fractional-order total variation
non-local total variation
alternating direction method of multipliers
regularization