Symmetric Face Normalization

Su, Ya; Liu, Zhe; Ban, Xiaojuan

doi:10.3390/sym11010096

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…However, in the actual repair process, the confidence value drops sharply and quickly approached zero, which makes the change trend of the priority in accordance with the change trend of the confidence   Cp, resulting in unreliable calculation of the priority and deviations, which affects the final repair effect. In order to ensure the continuity of the structural information of the restored image, according to the principle of local feature symmetry of the face image [34], this paper considers the surrounding information of the block to be repaired, introduces the symmetry between the block to be repaired and the neighborhood block, and uses it as the priority calculation. In part, if the neighborhood block shows similar information, then it is an extension of similar features.…”

Section: Calculation Of Priority Function Based On Feature Symmetrymentioning

confidence: 99%

An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

Niu

Yao

et al. 2020

Symmetry

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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.

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Section: Calculation Of Priority Function Based On Feature Symmetrymentioning

confidence: 99%

An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

Niu

Yao

et al. 2020

Symmetry

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“…Image registration is one of the most challenging tasks in image processing and is being widely used in many fields such as remote sensing [1][2][3], computer vision [4,5], and medical imaging [6][7][8]. In general, the aim of a registration method is to find a geometrical transformation to transform the coordinate of the source image to align with the reference image.…”

Section: Introductionmentioning

confidence: 99%

Space Independent Image Registration Using Curve-Based Method with Combination of Multiple Deformable Vector Fields

2019

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This paper proposes a novel curve-based or edge-based image registration technique that utilizes the curve transformation function and Gaussian function. It enables deformable image registration between images in different spaces, e.g., different color spaces or different medical image modalities. In particular, piecewise polynomial fitting is used to fit a curve and convert it to the global cubic B-spline control points. The transformation between the curves in the reference and source images are performed by using these control points. The image area is segmented with respect to the reference curve for the moving pixels. The Gaussian function, which is symmetric about the coordinates of the points of the reference curve, was used to improve the continuity in the intra- and inter-segmented areas. The overall result on curve transformation by means of the Hausdroff distance was 5.820 ± 1.127 pixels on average on several 512 × 512 synthetic images. The proposed method was compared with an ImageJ plugin, namely bUnwarpJ, and a software suite for deformable image registration and adaptive radiotherapy research, namely DIRART, to evaluate the image registration performance. The experimental result shows that the proposed method yielded better image registration performance than its counterparts. On average, the proposed method could reduce the root mean square error from 2970.66 before registration to 1677.94 after registration and can increase the normalized cross-correlation coefficient from 91.87% before registration to 97.40% after registration.

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