[1]王 君,唐贵进,刘小花,等.基于 Criminisi 的结构组稀疏表示图像修复算法[J].计算机技术与发展,2020,30(03):24-29.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 005]
　WANG Jun,TANG Gui-jin,LIU Xiao-hua,et al.Criminisi-based Structural Group Sparse Representation for Image Inpainting[J].Computer Technology and Development,2020,30(03):24-29.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 005]
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基于 Criminisi 的结构组稀疏表示图像修复算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年03期
页码:
24-29
栏目:
智能、算法、系统工程
出版日期:
2020-03-10

文章信息/Info

Title:
Criminisi-based Structural Group Sparse Representation for Image Inpainting
文章编号:
1673-629X(2020)03-0024-06
作者:
王 君; 唐贵进; 刘小花; 崔子冠
南京邮电大学 江苏省图像处理与图像通信重点实验室,江苏 南京 210003
Author(s):
WANG Jun; TANG Gui-jin; LIU Xiao-hua; CUI Zi-guan
Jiangsu Key Laboratory of Image Processing and Image Communication,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
图像修复; 稀疏表示; 字典学习; 结构组
Keywords:
image inpainting; sparse representation; dictionary learning; structural group
分类号:
TN911.7
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 03. 005
摘要:
结构组稀疏表示(structural group sparse representation,SGSR)算法对结构组的估计值进行奇异值分解得到字典,然后用 Split Bregman Iteration 算法求解优化模型得到稀疏解,最后借助字典和稀疏解来修复图像。 该算法在一定程度上解 决了传统稀疏表示算法忽略图像块之间相似性导致重构图像的结构和纹理不够自然的问题。 但该算法中,结构组的估计 值采用双线性插值算法得到,因此对块状缺失图像的修复效果一般。 为了更准确地计算结构组的估计值,提出用 Criminisi 算法代替双线性插值算法,并由此时的估计值生成更合理的字典和稀疏解,得到重构的结构组,进而更准确地修复图像。 实验数据表明,与SGSR算法相比,所提出的算法在峰值信噪比和相似结构性指数上分别平均提高了2.66dB和0.0017, 且在结构和纹理上取得了更自然的主观视觉效果。
Abstract:
The structural group sparse representation(SGSR) algorithm carries out singular value decomposition(SVD) on the estimate of a structural group to obtain the dictionary,then utilizes split Bregman iteration (SBI) algorithm to solve the optimization model for sparse coefficients,and finally adopts the dictionary and the coefficients to repair an image. In some sense,this algorithm solves the problem that the traditional sparse representation algorithm ignores the similarity between image patches,which will result in the fact that structures and textures in a reconstructed image are not natural enough. As the bilinear interpolation(BI) algorithm is used to calculate the estimate of a structural group,the SGSR algorithm does not fix the missing patch well. In this paper,in order to get the estimate of a structural group more accurately,we exploit the Criminisi algorithm to take the place of BI. It can obtain a more reasonable dictionary and coefficients from the estimate,and then reconstruct the structural group. Therefore,a better repaired image is produced. Experiment shows that compared with SGSR algorithm,the proposed algorithm is improved by 2.66 dB and 0.0017 respectively in terms of peak signal-to-noise ratio (PSNR) and similar structural index (SSIM),and it achieves more natural visual effects on textures of the reconstructed image.

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更新日期/Last Update: 2020-03-10