662 research outputs found
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
We address the problem of restoring a high-resolution face image from a
blurry low-resolution input. This problem is difficult as super-resolution and
deblurring need to be tackled simultaneously. Moreover, existing algorithms
cannot handle face images well as low-resolution face images do not have much
texture which is especially critical for deblurring. In this paper, we propose
an effective algorithm by utilizing the domain-specific knowledge of human
faces to recover high-quality faces. We first propose a facial component guided
deep Convolutional Neural Network (CNN) to restore a coarse face image, which
is denoted as the base image where the facial component is automatically
generated from the input face image. However, the CNN based method cannot
handle image details well. We further develop a novel exemplar-based detail
enhancement algorithm via facial component matching. Extensive experiments show
that the proposed method outperforms the state-of-the-art algorithms both
quantitatively and qualitatively.Comment: In IJCV 201
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal
Face Restoration (FR) aims to restore High-Quality (HQ) faces from
Low-Quality (LQ) input images, which is a domain-specific image restoration
problem in the low-level computer vision area. The early face restoration
methods mainly use statistic priors and degradation models, which are difficult
to meet the requirements of real-world applications in practice. In recent
years, face restoration has witnessed great progress after stepping into the
deep learning era. However, there are few works to study deep learning-based
face restoration methods systematically. Thus, this paper comprehensively
surveys recent advances in deep learning techniques for face restoration.
Specifically, we first summarize different problem formulations and analyze the
characteristic of the face image. Second, we discuss the challenges of face
restoration. Concerning these challenges, we present a comprehensive review of
existing FR methods, including prior based methods and deep learning-based
methods. Then, we explore developed techniques in the task of FR covering
network architectures, loss functions, and benchmark datasets. We also conduct
a systematic benchmark evaluation on representative methods. Finally, we
discuss future directions, including network designs, metrics, benchmark
datasets, applications,etc. We also provide an open-source repository for all
the discussed methods, which is available at
https://github.com/TaoWangzj/Awesome-Face-Restoration.Comment: 21 pages, 19 figure
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing
Given a really low-resolution input image of a face (say 16x16 or 8x8
pixels), the goal of this paper is to reconstruct a high-resolution version
thereof. This, by itself, is an ill-posed problem, as the high-frequency
information is missing in the low-resolution input and needs to be
hallucinated, based on prior knowledge about the image content. Rather than
relying on a generic face prior, in this paper, we explore the use of a set of
exemplars, i.e. other high-resolution images of the same person. These guide
the neural network as we condition the output on them. Multiple exemplars work
better than a single one. To combine the information from multiple exemplars
effectively, we introduce a pixel-wise weight generation module. Besides
standard face super-resolution, our method allows to perform subtle face
editing simply by replacing the exemplars with another set with different
facial features. A user study is conducted and shows the super-resolved images
can hardly be distinguished from real images on the CelebA dataset. A
qualitative comparison indicates our model outperforms methods proposed in the
literature on the CelebA and WebFace dataset.Comment: accepted in ACCV 202
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