We propose a very fast and effective one-step restoring method for blurry
face images. In the last decades, many blind deblurring algorithms have been
proposed to restore latent sharp images. However, these algorithms run slowly
because of involving two steps: kernel estimation and following non-blind
deconvolution or latent image estimation. Also they cannot handle face images
in small size. Our proposed method restores sharp face images directly in one
step using Convolutional Neural Network. Unlike previous deep learning involved
methods that can only handle a single blur kernel at one time, our network is
trained on totally random and numerous training sample pairs to deal with the
variances due to different blur kernels in practice. A smoothness
regularization as well as a facial regularization are added to keep facial
identity information which is the key to face image applications. Comprehensive
experiments demonstrate that our proposed method can handle various blur kenels
and achieve state-of-the-art results for small size blurry face images
restoration. Moreover, the proposed method shows significant improvement in
face recognition accuracy along with increasing running speed by more than 100
times.

我们提出了一种快速有效的一步恢复模糊人脸图像的方法，该方法基于卷积神经网络，可以处理各种大小的人脸图像，加入了光滑正则化和面部正则化来保持面部身份信息，取得了最先进的效果和提高了 100 倍的运行速度，并在人脸识别精度上有显著提高。

DeepDeblur: 快速一步模糊人脸图像复原

DeepDeblur: Fast one-step blurry face images restoration

Blind deblurring consists a long studied task, however the outcomes of
generic methods are not effective in real world blurred images. Domain-specific
methods for deblurring targeted object categories, e.g. text or faces,
frequently outperform their generic counterparts, hence they are attracting an
increasing amount of attention. In this work, we develop such a domain-specific
method to tackle deblurring of human faces, henceforth referred to as face
deblurring. Studying faces is of tremendous significance in computer vision,
however face deblurring has yet to demonstrate some convincing results. This
can be partly attributed to the combination of i) poor texture and ii) highly
structure shape that yield the contour/gradient priors (that are typically
used) sub-optimal. In our work instead of making assumptions over the prior, we
adopt a learning approach by inserting weak supervision that exploits the
well-documented structure of the face. Namely, we utilise a deep network to
perform the deblurring and employ a face alignment technique to pre-process
each face. We additionally surpass the requirement of the deep network for
thousands training samples, by introducing an efficient framework that allows
the generation of a large dataset. We utilised this framework to create 2MF2, a
dataset of over two million frames. We conducted experiments with real world
blurred facial images and report that our method returns a result close to the
sharp natural latent image.

本文提出了一种基于领域特定的方法来解决人脸去模糊问题，使用深度网络和人脸对齐来处理每张图片，并采用了有效的方法生成了超过 2 百万帧的数据集，实验显示该方法在真实世界的人脸去模糊任务中取得了接近于高清自然图片的效果。

深度人脸去模糊

Deep Face Deblurring

Non-uniform blind deblurring for general dynamic scenes is a challenging
computer vision problem as blurs arise not only from multiple object motions
but also from camera shake, scene depth variation. To remove these complicated
motion blurs, conventional energy optimization based methods rely on simple
assumptions such that blur kernel is partially uniform or locally linear.
Moreover, recent machine learning based methods also depend on synthetic blur
datasets generated under these assumptions. This makes conventional deblurring
methods fail to remove blurs where blur kernel is difficult to approximate or
parameterize (e.g. object motion boundaries). In this work, we propose a
multi-scale convolutional neural network that restores sharp images in an
end-to-end manner where blur is caused by various sources. Together, we present
multi-scale loss function that mimics conventional coarse-to-fine approaches.
Furthermore, we propose a new large-scale dataset that provides pairs of
realistic blurry image and the corresponding ground truth sharp image that are
obtained by a high-speed camera. With the proposed model trained on this
dataset, we demonstrate empirically that our method achieves the
state-of-the-art performance in dynamic scene deblurring not only
qualitatively, but also quantitatively.

本文提出了一种多尺度卷积神经网络模型，用于处理不同来源的模糊，通过使用自定义的大规模数据集训练该模型，实现了在动态场景去模糊方面的卓越性能。