Joint face hallucination and deblurring via structure generation and detail enhancement

Publication Type

Journal Article

Publication Date

6-2019

Abstract

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.

Keywords

Face hallucination, Face deblurring, Convolutional Neural Network

Discipline

Information Security

Research Areas

Information Systems and Management

Publication

International Journal of Computer Vision

Volume

127

Issue

6-7

First Page

785

Last Page

800

ISSN

0920-5691

Identifier

10.1007/s11263-019-01148-6

Publisher

Springer

Additional URL

https://doi.org/10.1007/s11263-019-01148-6

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