Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (4): 131-135.doi: 10.11896/jsjkx.190300149

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Adaptive Image Inpainting Based on Structural Correlation

ZHOU Xian-chun1,2, XU Yan1   

  1. 1 School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;
    2 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science andTechnology,Nanjing 210044,China
  • Received:2019-03-27 Online:2020-04-15 Published:2020-04-15
  • Contact: XU Yan,born in 1995,postgraduate.Her main research interests include ima-ge processing and so on.
  • About author:ZHOU Xian-chun,born in 1974,Ph.D,associate professor,postgraduate supervision.His main research interests include signal ＆ information processing,and digital image processing.
  • Supported by:
    This work as supported by the National Natural Science Foundation of China(11202106,61302188),“Information and Communication Engineering” Superiority Discipline Construction Project of Jiangsu Province and Jiangsu Brand Professional Construction Project.

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Abstract: This paper proposed an adaptive image inpainting algorithm based on structural correlation to solve the problems of inaccurate priority function and degraded image quality in Criminisi inpainting algorithm.First,the structural correlation is introduced to improve the priority calculation and increase the reliability of the priority calculation.Then,the sample size is adaptively selected to make the repair more accurate and improve the efficiency of repair.Finally,HSV color space is introduced,and according to the chromaticity and brightness of sample,the optimal matching block is searched to reduce the repair error and complete the image restoration.Experimental results show that the proposed algorithmhas obvious improvement in subjective visual compared to Criminisi repair algorithm,its peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are improved,and repair effect is better.Compared with the traditional Criminisi inpainting algorithm,the peak signal-to-noise ratio of proposed algorithm is improved by 1~3dB and structure similarity is closer to 1.This algorithm uses structure correlation adaptively to select sample block size to repair color broken images,making the priority calculation more reasonable and accurate and the repair effect better,which is helpful to practical application.

Key words: Adaptive sample block, HSV color space, Image inpainting, Structural dependence

CLC Number: 

  • TP391
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