Adaptive Image Inpainting Algorithm Based on Patch Structure Sparsity

LI Zhi-dan; HE Hong-jie; YIN Zhong-ke; CHEN Fan; RENQING Nuo-bu

doi:10.3969/j.issn.0372-2112.2013.03.022

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Adaptive Image Inpainting Algorithm Based on Patch Structure Sparsity

更新时间：2025-07-16

- Adaptive Image Inpainting Algorithm Based on Patch Structure Sparsity
- Acta Electronica Sinica Vol. 41, Issue 3, Pages: 549-554(2013)
- 作者机构：
  
  1. 西南交通大学信号与信息处理四川省重点实验室,四川,成都,610031
  2. 西藏大学工学院计科系,西藏,拉萨,850000
  3. 西南交通大学信号与信息处理四川省重点实验室,四川,成都,610031
  4. 西藏大学工学院计科系,西藏,拉萨,850000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.60970122);Fundamental Research Funds for the Central Universities (No.SWJTU09CX039, No.SWJTU10CX09)
- DOI：10.3969/j.issn.0372-2112.2013.03.022
  CLC： TP391.41
- Published：2013
- 稿件说明：
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LI Zhi-dan, HE Hong-jie, YIN Zhong-ke, et al. Adaptive Image Inpainting Algorithm Based on Patch Structure Sparsity[J]. Acta Electronica Sinica, 2013, 41(3): 549-554.
DOI：

LI Zhi-dan, HE Hong-jie, YIN Zhong-ke, et al. Adaptive Image Inpainting Algorithm Based on Patch Structure Sparsity[J]. Acta Electronica Sinica, 2013, 41(3): 549-554. DOI： 10.3969/j.issn.0372-2112.2013.03.022.

摘要

现有基于稀疏性的图像修复算法采用固定大小的待填充块和邻域一致性约束

且在全局搜索待填充块的最优匹配块

既降低了待修复区域的结构连贯性和纹理清晰性

又增加了算法的时间复杂度.针对上述问题

根据破损区域特性和块结构稀疏度间的关系

提出基于块结构稀疏度的自适应图像修复算法.根据最大优先权值点的块结构稀疏度值

设定不同参数以自适应选取待填充块大小、邻域一致性约束权重系数和局部搜索区域大小

并通过仿真实验分析讨论了各参数选取.实验结果表明本文算法较文献算法在峰值信噪比上提高0.3dB~1.2dB

并且提高算法速度3~7倍.

Abstract

In the existing patch sparsity based image inpainting algorithms

the exemplar-size and neighborhood-consistence weight are fixed

and the best match patches of the patch to be filled are searched in the whole source region.However

it decreases the connectivity of structure and clearness of texture while increases the time complexity of this algorithm.To address these problems

an adaptive image inpainting algorithm is proposed based on patch structure sparsity

in the light of the relationship between the characteristics of damage region and patch structure sparsity.According to patch structure sparsity value of the point which has the maximal prority value

the size of patch to be filled

the neighborhood consistence weight and the part-search region size are adaptively confirmed through setting some parameters

then these parameters are analysed and discussed by some experiments.Experimental results show that the PSNR is increased by 0.3~1.2dB and the speed is improved by 3~7 times compared with the existing algorithms.

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