Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (12): 3541-3546.DOI: 10.11772/j.issn.1001-9081.2017.12.3541

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Image inpainting algorithm for partitioning feature subregions

LI Mengxue1, ZHAI Donghai1,2, MENG Hongyue1, CAO Daming1   

  1. 1. School of Information Science and Technology, Southwest Jiaotong University, Chendu Sichuan 610031, China;
    2. School of Engineering, Tibet University, Lhasa Tibet 850000, China
  • Received:2017-06-02 Revised:2017-09-06 Online:2017-12-10 Published:2017-12-18
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61461048).


李梦雪1, 翟东海1,2, 孟红月1, 曹大命1   

  1. 1. 西南交通大学 信息科学与技术学院, 成都 610031;
    2. 西藏大学 工学院, 拉萨 850000
  • 通讯作者: 李梦雪
  • 作者简介:李梦雪(1992-),女,重庆人,硕士研究生,主要研究方向:数字图像处理;翟东海(1974-),男,山西芮城人,副教授,博士,主要研究方向:数字图像处理、海量数据挖掘;孟红月(1993-),女,河南永城人,硕士研究生,主要研究方向:数字图像处理;曹大命(1990-),男,河北沧州人,硕士研究生,主要研究方向:数字图像处理。
  • 基金资助:

Abstract: In order to solve the problem of inpainting missing information in the large damaged region with rich texture information and complex structure information, an image inpainting algorithm for partitioning feature subregions was proposed. Firstly, according to the different features contained in the image, the feature formula was used to extract the features, and the feature subregions were divided by the statistical eigenvalues to improve the speed of image inpainting. Secondly, on the basis of the original Criminisi algorithm, the calculation of priority was improved, and the structural fracture was avoided by increasing the influence of the structural term. Then, the optimal sample patch set was determined by using the target patch and its optimal neighborhood similar patches to constrain the selection of sample patch. Finally, the optimal sample patch was synthesized by using weight assignment method. The experimental results show that, compared with the original Criminisi algorithm, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm is improved by 2-3 dB; compared with the patch priority weight computation algorithm based on sparse representation, the inpainting efficiency of the proposed algorithm is also obviously improved. Therefore, the proposed algorithm is not only suitable for the inpainting of small-scale damaged images, but also has better inpainting effect for large damaged images with rich texture information and complex structure information, and the restored images are more in line with people's visual connectivity.

Key words: image inpainting, feature extraction, Criminisi algorithm, priority, sample patch

摘要: 为了解决含有丰富纹理信息和复杂结构信息的大破损区域中的缺失信息修复的问题,提出了一种划分特征子区域的图像修复算法。首先,根据图像中包含的不同特征,运用特征公式进行特征提取,再通过统计特征值划分特征子区域,提高了图像修复的速度;其次,在原Criminisi算法的基础上改进了优先级的计算,通过增大结构项的影响,避免结构断裂的产生;然后,通过目标块和其最佳邻域相似块共同约束样本块的选取,确定最佳样本块集;最后,利用权值分配法合成最佳样本块。实验结果表明,所提算法相比原Criminisi算法,其峰值信噪比(PSNR)提升了2~3 dB,相比基于稀疏表示的块优先权值计算的算法,其修复效率有明显的提高。所提算法不但适用于一般小尺度的破损图像的修复,而且对于含有丰富纹理信息和复杂结构信息的大破损图像的修复效果也更佳,并且修复后的图像更加符合人们视觉上的连通性。

关键词: 图像修复, 特征提取, Criminisi算法, 优先级, 样本块

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