Image restoration based on adaptive group images sparse regularization

Zong-yue WANG; Qi-ming XIA; Guo-rong CAI; Jin-he SU; Jie-min ZHANG

doi:10.3788/OPE.20192712.2713

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Image restoration based on adaptive group images sparse regularization

Information Sciences | 更新时间：2020-07-09

- Image restoration based on adaptive group images sparse regularization
- Optics and Precision Engineering Vol. 27, Issue 12, Pages: 2713-2721(2019)
- 作者机构：
  
  集美大学计算机工程学院, 福建厦门 361021
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192712.2713
  CLC： TP394.1; TH691.9
- Received：19 July 2019，
  
  Accepted：07 September 2019，
  
  Published：25 December 2019
- 稿件说明：
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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：

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

吴俊, 汪源源, 陈悦, 等.基于同质区域自动选取的各向异性扩散超声图像去噪[J].光学精密工程, 2014, 22(5):1312-1321.

WU J, WANG Y Y, CHEN Y, et al .. Speckle reduction of ultrasound images with anisotropic diffusion based on homogeneous region automatic selection[J]. Opt. Precision Eng. , 2014, 22(5):1312-1321. (in Chinese)

ZHANG X J, YE W ZH.An adaptive second-order partial differential equation based on TV equation and p-Laplacian equation for image denoising[J]. Multimedia Tools and Applications , 2019, 78(13): 18095-18112.

刘超, 张晓晖.超低照度下微光图像的深度卷积自编码网络复原[J].光学精密工程, 2018, 26(4):951-961.

LIU CH, ZHANG X H. Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination[J]. Opt. Precision Eng. , 2018, 26(4):951-961. (in Chinese)

SHEN J, CHAN T F. Mathematical models for local nontexture inpaintings[J]. SIAM Journal on Applied Mathematics , 2002, 62(3): 1019-1043.

FIGUEIREDO M A T, LEITAO J M N. Unsupervised image restoration and edge location using compound Gauss-Markov random fields and the MDL principle[J]. IEEE Transactions on Image Processing , 1997, 6(8): 1089-1102.

TANAKA K, HORIGUCHI T. Solvable Markov random field model in color image restoration[J]. Physical Review E , 2002, 65(4): 46-142.

计宏磊, 杨清文.基于群结构稀疏表示的图像修复[J].计算机工程与应用, 2016, 52(18):14-17.

JI H L, YANG Q W. Image inpainting algorithm based on group-structured sparse representation[J]. Computer Engineering and Applications , 2016, 52(18):14-17.(in Chinese)

AHARONM, MICHAEL E, ALFRED B. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on signal processing , 2006, 54(11): 4311-4322.

DONG W SH, SHI G M, LI X. Nonlocal image restoration with bilateral variance estimation: a low-rank approach[J]. IEEE transactions on image processin , 2012, 22(2): 700-711.

DONG W SH, ZHANG L, SHI G M, et al .. Nonlocally centralized sparse representation for image restoration[J]. IEEE transactions on Image Processing, 2012, 22(4): 1620-1630.

仇翔, 戴明.基于L0稀疏先验的相机抖动模糊图像盲复原[J].光学精密工程, 2017, 25(9):2490-2498.

QIU X, DAI M. Blind restoration of camera shake blurred image based on L0 sparse priors[J]. Opt. Precision Eng. , 2017, 25(9): 2490-2498.(in Chinese)

ELAD M, STARCK J L, QUERRE P, et al .. Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)[J]. Applied and Computational Harmonic Analysis , 2005, 19(3): 340-358.

ZHANG J, ZHAO C, ZHAO D, et al .. Image compressive sensing recovery using adaptively learned sparsifying basis via LO minimization[J]. Signal Processing , 2014, 103(10): 114-126.

刘伟豪, 梅林, 蔡烜.稀疏梯度先验模型的正则化图像修复[J].中国图象图形学报, 2012, 17(12):1485-1491.

LIU W H, MEI L, CAI X. Regularized image restoration algorithm on sparse gradient prior model[J]. Journal of Image and Graphics , 2012, 17(12):1485-1491. (in Chinese)

HE L, WANG, YILUN. Iterative support detection-based split bregman method for wavelet frame-based image inpainting[J]. IEEE Transactions on Image Processing , 2014, 23(12):5470-5485.

MAHDI D., ISABEL N., GIL G. Spatially adaptive total variation deblurring with split Bregman technique[J]. IET Image Processing , 2018, 12(6): 948-958.

ZHANG J, ZHAO D, GAO W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing , 2014, 23(8): 3336-3351.

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