Nowadays remarkable progress has been observed in facial detection as a core part of computer vision. Nevertheless, motion blur still presents substantial challenges in face detection. The most recent face image deblurring methods make oversimplifying presumption and fail to restore the highly structured face shape/identity information. Therefore, we propose a data-driven based face image deblurring approach that foster facial detection and identity preservation. The proposed model includes two sequential data streams: Out of any supervision the first has been trained on real unlabeled clear/blurred data to generate a close realistic blurred image data during its inference. On the other hand, the generated labeled data has been exploited with by a second supervised learning-based data steam to learn the mapping function from blur domain to the clear one. We utilize the restored data to conduct an experimentation on face detection task. The experimental evaluation demonstrates the outperformance of our results and supports our system design and training strategy.
Exploiting Semantics for Face Image Deblurring
