一种双约束稀疏模型图像修复算法

史金钢; 齐春

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一种双约束稀疏模型图像修复算法

更新时间：2025-07-09

- 一种双约束稀疏模型图像修复算法
- An Image Inpainting Algorithm Based on Sparse Modeling with Double Constraints
- 西安交通大学学报 2012年46卷第2期页码：6-10+16
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2011-08-05. 作者简介: 史金钢(1988-),男,博士生;齐春(通信作者), 男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(60972124)
- DOI：
  中图分类号： TP391
- 网络首发：2012-02-10，
  
  纸质出版：2012
- 稿件说明：
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史金钢, 齐春. 一种双约束稀疏模型图像修复算法[J]. 西安交通大学学报, 2012,46(2):6-10+16.

An Image Inpainting Algorithm Based on Sparse Modeling with Double Constraints[J]. 2012, 46(2): 6-10+16.
史金钢, 齐春. 一种双约束稀疏模型图像修复算法[J]. 西安交通大学学报, 2012,46(2):6-10+16. DOI：

An Image Inpainting Algorithm Based on Sparse Modeling with Double Constraints[J]. 2012, 46(2): 6-10+16. DOI：

摘要

针对图像处理中需要修复大面积缺损区域的问题

提出一种基于双约束稀疏模型的图像修复算法. 该方法首先在已知区域内搜索与待填充目标块相似的样本

将每个样本块都视为一个高维向量

则相似的样本在高维空间中都在目标块的邻域内. 假设邻域中的样本处于同一流形上

使用局部线性嵌入方法对未知区域进行估计

然后利用稀疏表示模型得到最终结果. 实验结果表明

与传统的基于样本块的修复方法相比较

使用该算法修复后的图像纹理和结构信息更加清晰.

Abstract

An exemplar-based inpainting algorithm via sparse representation is presented to focus on the problem of removing large objects from digital image. Exemplars that are similar with the target patch are searched in known regions. These exemplars are all neighbors of the target patch in a high-dimensional data space by regarding each of them as a high-dimensional vector. The unknown region is estimated through locally linear embedding method by supposing these neighbors in a same manifold

and then

sparse representation is applied to enforce compatibility and sharpness with the surrounding region. Experimental results and comparison with the traditional exemplar-based inpainting algorithm show that the proposed algorithm can effectively repair the texture and structure information in the damaged image.

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references

BERTALMIO M, SAPIRO G, CASELLES V, et al. Image inpainting [C]∥Proc of SIGGRAPH. New York, USA: ACM, 2000: 417-424.

EFROS A, LEUNG T. Texture synthesis by non-parametric sampling [C]∥Proc of ICCV. Piscataway, NJ, USA: IEEE, 1999: 1033-1038.

CRIMINISI A, PEREZ P, TOYAMA K. Region filling and object removal by exemplar-based image inpainting [J]. IEEE Transactions on Image Processing, 2004, 13(9):1200-1212.

WONG A, ORCHARD J. A nonlocal-means approach to exemplar-based inpainting [C]∥Proc of ICIP. Piscataway, NJ, USA: IEEE, 2008: 2600-2603.

XU Zongben, SUN Jian. Image inpainting by patch propagation using patch sparsity [J]. IEEE Transactions on Image Processing, 2010, 19(5):1153-1165.

ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000,290(5500): 2323-2326.

MA Xiang, ZHANG Junping, QI Chun. Hallucinating face by position-patch [J]. Pattern Recognition, 2010, 43(6): 2224-2236.

MA Xiang, HUANG Hua, WANG Shaopeng, et al. A simple approach to multiview face hallucination [J]. IEEE Signal Processing Letters, 2010, 17(6): 579-582.

马祥,齐春.全局重建和位置块残差补偿的人脸超分辨率算法[J].西安交通大学学报,2010,44(4):9-12.

MA Xiang, QI Chun. A face image super-resolution algorithm based on global reconstruction and position-patch residue compensation [J]. Journal of Xi'an Jiaotong University, 2010, 44(4): 9-12.

TROPP J, WRIGHT S. Computational methods for sparse solution of linear inverse problems [J]. Proceedings of the IEEE, 2010, 98(6):948-958.

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