A method of inpainting moles and acne on the high‐resolution face photos

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.

SemID: Blind Image Inpainting with Semantic Inconsistency Detection

“…Various existing scene removal tasks boil down to the same path as blind restoration, such as removing raindrops [33] and snow curtains [34] from images of natural scenes or eliminating moles, acnes, and wrinkles to beautify face photos [35] . These tasks share similar assumptions that the acquired images are immediate and unique, differing in that the feature statistics of noisy regions are influenced by some strong priors.…”