Progressive Semantic Face Deblurring

Previous face deblurring methods have utilized semantic segmentation maps as prior knowledge. Most of these methods generated the segmentation map from a blurred facial image, and restore it using the map in a sequential manner. However, the accuracy of the segmentation affects the restoration performance. Generally, it is difficult to obtain an accurate segmentation map from a blurred image. Instead of sequential methods, we propose an efficient method that learns the flows of facial component restoration without performing segmentation. To this end, we propose a multi-semantic progressive learning (MSPL) framework that progressively restores the entire face image starting from the facial components such as the skin, followed by the hair, and the inner parts (eyes, nose, and mouth). Furthermore, we propose a discriminator that observes the reconstruction-flow of the generator. In addition, we present new test datasets to facilitate the comparison of face deblurring methods. Various experiments demonstrate that the proposed MSPL framework achieves higher performance in facial image deblurring compared to the existing methods, both qualitatively and quantitatively. Our code, trained model and data are available at https://github.com/dolphin0104/MSPL-GAN.

Abstract licence: CC BY-NC-ND

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