| 张志伟,马杰,夏克文,杨帆.一种应用于图像修复的非负字典学习算法[J].光电子激光,2014,(8):1613~1619 |
| 一种应用于图像修复的非负字典学习算法 |
| A novel image inpainting algorithm using non-negative dictionary learning |
| 投稿时间：2014-02-21 |
| DOI： |
| 中文关键词: 字典学习 稀疏 图像修复 非负矩阵分解(NMF) |
| 英文关键词:dictionary learing sparseness image inpainting non-negative matrix factoriza tion |
| 基金项目:国家自然科学基金(61203245、51208168)、河北省自然基金(F2012202027、F2012202116)和河北省高层次人才资助项目(C2012003038)资助项目 (河北工业大学 信息工程学院,天津 300130) |
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| 中文摘要: |
| 提出了一种基于非负稀疏字典学习的图像修复算法 ,在非负矩阵分解(NMF)的目标函数中增加稀 疏约束项,再通过稀疏编码和字典更新两步迭代学习得到训练样本的非负字典,稀疏编码采 用的是非负 正交匹配追踪(OMP)算法,字典更新则类似经典的KSVD算法；最终根据字典通过光滑L0范数 算法得到待修复图像的稀 疏系数,进而实现图像的修复。图像修复实验结果表明,本文算法能够对不同类型缺失的图 像做到较好的修复,修复的视觉效果和技术指标都优于当前主流算法。 |
| 英文摘要: |
| Various algorithms have been proposed for image inpainting,among which sparse representations of image inpaintin g via an overcomplete dictionary have recently produced promising results.Since the images are all non-negative,the traditional dictionary learning methods imposed simply with non-negativity may become inapplicable.In this paper,a non-negative dictionary learning method for image inpainting is presented,and the proposed method enforces sparseness by penalizing the L0norm of the coefficient matrix in non-negative factorization objective function.The method consists of two steps:a sparse coding and a dictionary update stag.A nonnegative version of orthogonal matching pursuit (OMP) is used for s parse coding,and the dictionary update resembles standard K-SVD.With learned dictionary,Smoothed L0(SL0) algori thm is used for image reconstruction.Numerical experiments were conducted,and the results d emonstrate that the proposed inpainting method outperforms conventional K-SVD image inpainting algorithm and several other algorithms for comprasion,and it achieves state-of-art inpainting performance in terms of b oth peak signal to noise ratio (PSNR) and structural similarity index measurement(S SIM).What’s more,its computation c onsumption is fairly low. |
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