A method of inpainting moles and acne on the high‐resolution face photos. Issue 3 (21st December 2020)

• Record Type: Journal Article
• Authors: Li, Xuewei; Li, Xueming; Zhang, Xianlin; Liu, Yang; Liang, Jiayi; Guo, Ziliang; Zhai, Keyu
• Is Part Of: IET image processing. Volume 15:Issue 3(2021)
• Page Start: 833
• Page End: 844
• Publication Date: 2020-12-21
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12066
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: With the rapid development of mobile phones, more and more high‐resolution photos are taken. The demand for high‐resolution image inpainting is becoming increasingly urgent. In order to repair high‐resolution face images automatically and quickly, this paper proposes an improved generative adversarial networks method. Firstly, we made a high‐resolution dataset for training and testing, and abandoned the traditional 256 × 256 size data. Secondly, since the existing methods can only repair the mask with fixed size and shape on the image, when the global average pooling layer is used in the network, the improved network can repair the moles and acne with arbitrary sizes and shapes on the human face photos. Finally, in order to achieve optimal performance of the network, a mixed loss function is used in training. The experimental results prove that our method has not only achieved good results in qualitative results, but also achieved excellent results in quantitative results.