![]() ![]() ![]() added a texture constraint while retaining the context encoder. Since then, many researchers improved it. It can obtain structural features and semantic information of images, and can produce reasonable details, but the generated texture details are not fine enough, have obvious boundaries, and cannot be used for high-resolution images. This work is one of the earliest works of using deep neural networks for image repair. proposed a context encoder, which includes an encoder to capture images with missing parts and generate potential feature representations, and a decoder which uses the latent feature representations to generate missing part images, using Euclidean distance and adversarial loss function. In the image inpainting methods based on deep learning, originally Pathak et al. This is used to remove the large objects in the image as the target, while the texture information of the non-target area is used to synthesize the unobstructed original background in the image. This type of method was originally proposed by Criminisi et al. The principle of the sample block-based texture synthesis image repair method constructs the repair priority from the information of the non-target region, and searches for the best matching block for the highest priority block to fill the target region to complete the image repair. The PDE-based method targets small areas of unstructured defect areas (such as scratches) and has a good inpainting effect, but it is not suitable for large missing images that contain complex structural information. ![]() There are two classic methods: a method based on total variation (TV) and a method based on curvature-driven diffusion (CDD). In the image inpainting methods based on graphics, the PDE-based structure propagation method combines smooth prior knowledge to transfer the structural information from the outside to the inside of the target area to complete the image inpainting. 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. 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. 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. 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. 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. 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. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. 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. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. 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. Face image inpainting technology is an important research direction in image restoration. ![]()
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