计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 241-247.doi: 10.19678/j.issn.1000-3428.0049463

• 图形图像处理 • 上一篇    下一篇

基于改进非局部均值的分数阶全变分算法

封晨波,覃亚丽,陈辉,常丽萍,薛林林   

  1. 浙江工业大学 信息工程学院,杭州 310023
  • 收稿日期:2017-11-28 出版日期:2019-04-15 发布日期:2019-04-15
  • 作者简介:封晨波(1993—),女,硕士研究生,主研方向为图像处理、光场成像;覃亚丽,教授;陈辉,硕士研究生;常丽萍,副教授;薛林林,讲师。
  • 基金项目:

    国家自然科学基金(61675184,61405178);浙江省自然科学基金(LY18F010023)。

Fractional Total Variation Algorithm Based on Improved Non-local Means

FENG Chenbo,QIN Yali,CHEN Hui,CHANG Liping,XUE Linlin   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2017-11-28 Online:2019-04-15 Published:2019-04-15

摘要:

传统全变分算法在变分过程中多数会受到阶梯效应的影响,导致重构图像出现纹理缺失和过平滑。为此,提出一种基于改进非局部均值的重构算法。通过引入分数阶梯度模型保留图像纹理信息,利用非局部均值滤波法更新拉格朗日梯度算子,从而降低计算复杂度。实验结果表明,与传统TVAL3算法相比,该算法能够有效减少运行时间,具有较好的重构性能。

关键词: 压缩感知, 分数阶微分, 全变分, 非局部均值, 阶梯效应

Abstract:

The traditional total variation algorithm is mostly affected by the staircase effect in the variation process,so it causes texture loss and over-smoothing in the reconstructed image.Therefore,a reconstruction algorithm based on improved non-local means is proposed.The image texture information is preserved by introducing a fractional step model,and the Lagrangian gradient operator is updated by the non-local means filtering method,thereby reducing the computational complexity.Experimental results show that compared with the traditional TVAL3 algorithm,this algorithm can effectively reduce the running time and has better reconstruction performance.

Key words: compressive sensing, fractional differential, total variation, non-local means, staircase effect

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