Facial skin beautification via sparse representation over learned layer dictionary
In this paper, we propose a facial skin beautification framework to remove facial spots based on layer dictionary learning and sparse representation. More precisely, we first decompose the face image into three layers: lighting layer, detail layer and color layer. The corresponding detail layer dictionary are learned by using 60 thousands beauty images collected from the Internet. Thereafter, the detail layer of the image is reconstructed by using sparse representation. Moreover, a binary mask obtained from the learned layer is used to transform detail information from original detail layer to the learned one. The experiment results demonstrate that the proposed method is more effective in eliminating moles, flaws and wrinkles in face image compared with representative commercial systems like PicTreat, Portrait+, Portraitrue and MeituPic.