Next Article in Journal
New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making
Previous Article in Journal
FPGA Implementation and Design of a Hybrid Chaos-AES Color Image Encryption Algorithm
Open AccessArticle

An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

1
College of Electrical Engineering, Guizhou University, Guiyang 550025, China
2
State Grid Sichuan Tianfu New District Power Supply Company, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 190; https://doi.org/10.3390/sym12020190
Received: 26 December 2019 / Revised: 8 January 2020 / Accepted: 10 January 2020 / Published: 22 January 2020
Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. After that, in the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face. Finally, we introduced the HSV (Hue, Saturation, Value) color space to determine the best matching block according to the chroma and brightness of the sample, reduce the repair error, and complete the face image inpainting. During the experiment, we firstly performed visual evaluation and texture analysis on the inpainting face image, and the results show that the face image inpainting by our algorithm maintained the consistency of the face structure, and the visual observation was closer to the real face features. Then, we used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as objective evaluation indicators; among the five sample face images inpainting results given in this paper, our method was better than the reference methods, and the average PSNR value improved from 2.881–5.776 dB using our method when inpainting 100 face images. Additionally, we used the time required for inpainting the unit pixel to evaluate the inpainting efficiency, and it was improved by 12%–49% with our method when inpainting 100 face images. Finally, by comparing the face image inpainting experiments with the generative adversary network (GAN) algorithm, we discuss some of the problems with the method in this paper based on graphics in repairing face images with large areas of missing features. View Full-Text
Keywords: face image inpainting; feature symmetry; adaptive; HSV color space face image inpainting; feature symmetry; adaptive; HSV color space
Show Figures

Figure 1

MDPI and ACS Style

Niu, Z.; Li, H.; Li, Y.; Mei, Y.; Yang, J. An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry. Symmetry 2020, 12, 190.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop