自适应图像组的稀疏正则化图像复原

王宗跃; 夏启明; 蔡国榕; 苏锦河; 张杰敏

doi:10.3788/OPE.20192712.2713

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自适应图像组的稀疏正则化图像复原

信息科学 | 更新时间：2020-07-09

- 自适应图像组的稀疏正则化图像复原
- Image restoration based on adaptive group images sparse regularization
- 光学精密工程 2019年27卷第12期页码：2713-2721
- 作者机构：
  
  集美大学计算机工程学院, 福建厦门 361021
- 作者简介：
  
  [ "王宗跃(1979-)，男，福建漳州人，博士，副教授，2003年于武汉理工大学获得学士学位，2009年于武汉大学获得博士学位，主要从事模式识别、计算机视觉、遥感数据处理等研究。E-mail：wangzongyue@jmu.edu.cn" ]
  [ "张杰敏(1964-)，女，山西太原人，硕士，教授，1987年于南京理工大学获得学士学位，1996年于复旦大学获得硕士学位，主要从事智能信息处理、信息安全等方向的研究。E-mail：zhangjm@jmu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(61672335);国家重点研发项目资助(2016YFC0502902);国家自然科学基金资助项目(61701191);广东省创新强校项目资助(2017KCXTD015);广东省创新强校项目资助(2016KZDXM012);福建省科技厅高校产学研重大项目资助(2017H6015);福建省自然科学基金资助项目(2018J05108);福建省科技计划项目资助(2019R0068);厦门市产学研协同创新及科技合作项目资助(3502Z20183032)
- DOI：10.3788/OPE.20192712.2713
  中图分类号： TP394.1; TH691.9
- 收稿日期：2019-07-19，
  
  录用日期：2019-9-7，
  
  纸质出版日期：2019-12-25
- 稿件说明：
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王宗跃, 夏启明, 蔡国榕, 等. 自适应图像组的稀疏正则化图像复原[J]. 光学精密工程, 2019,27(12):2713-2721.

Zong-yue WANG, Qi-ming XIA, Guo-rong CAI, et al. Image restoration based on adaptive group images sparse regularization[J]. Optics and precision engineering, 2019, 27(12): 2713-2721.
王宗跃, 夏启明, 蔡国榕, 等. 自适应图像组的稀疏正则化图像复原[J]. 光学精密工程, 2019,27(12):2713-2721. DOI： 10.3788/OPE.20192712.2713.

Zong-yue WANG, Qi-ming XIA, Guo-rong CAI, et al. Image restoration based on adaptive group images sparse regularization[J]. Optics and precision engineering, 2019, 27(12): 2713-2721. DOI： 10.3788/OPE.20192712.2713.

摘要

基于图像组的稀疏正则化图像复原方法采用自适应的结构组字典来代替传统的基于整幅图像块的学习字典，既能够更好的学习局部特征又显著降低字典学习的时间复杂度；然而，因算法中的一些参数还未优化，使得算法复杂度还比较高。因此，本文提出了基于粗糙度的自适应图像组的稀疏正则化图像复原方法。首先，计算图像的全局粗糙度和局部粗糙度；然后，根据全局的粗糙度计算自适应调整正则化的迭代次数，根据局部的粗糙度调整学习字典所需的样本数；最后，将自适应调整出的参数应用于基于图像组的稀疏正则化的图像复原中。将本文所提出的方法应用到不同平滑度图像的去文字图像复原案例中，实验结果表明，在保证相近的复原效果下，能够大幅度提升效率，尤其在较为平滑的图像中能够达到接近30倍的加速比。

Abstract

The sparse regularized image restoration method based on animage group adopts the adaptive structure group dictionary to replace the traditional learning dictionary based on the entireimage block.However

because some parameters in the algorithm have not been optimized

the complexity of the algorithm remains relatively high.Therefore

this study proposed a sparse regularization image restoration method based on an adaptive image group in terms of roughness.First

global and local image roughnesses were calculated.Then

the number of self-adaptive regularization iterations was calculated according to the global roughness

and the number of samples required for learning the dictionary was adjusted based on the local roughness.Finally

the adaptive parameters were applied to the process of sparse regularization image restoration based on an image group.The method proposed in this study was applied to a case involving image restoration of text removal for images with different degrees of smoothness. The experimental results show that the efficiency of image restoration can be greatly improved when a similar restoration effect is guaranteed

particularly in relatively smooth images

where the speed-up ratio can reach nearly 30 times.

关键词

Keywords

references

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