Deep Semantic Face Deblurring
This addresses the problem of restoring blurry face images for applications like recognition, but it is incremental as it builds on existing CNN-based deblurring with semantic priors.
The paper tackles face image deblurring by using semantic cues in a deep CNN, resulting in restored sharp images with improved facial details and favorable performance against state-of-the-art methods in quality, recognition, and speed.
In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs). As face images are highly structured and share several key semantic components (e.g., eyes and mouths), the semantic information of a face provides a strong prior for restoration. As such, we propose to incorporate global semantic priors as input and impose local structure losses to regularize the output within a multi-scale deep CNN. We train the network with perceptual and adversarial losses to generate photo-realistic results and develop an incremental training strategy to handle random blur kernels in the wild. Quantitative and qualitative evaluations demonstrate that the proposed face deblurring algorithm restores sharp images with more facial details and performs favorably against state-of-the-art methods in terms of restoration quality, face recognition and execution speed.