Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing
the resolution of low-resolution (LR) face images to generate high-resolution face images, is …

Existing methods of 3D dthus limiting the scope of their practical applications. In this paper,
we propose a novel regression framework which makes a balance among speed, accuracy …

This paper is on image and face super-resolution. The vast majority of prior work for this
problem focus on how to increase the resolution of low-resolution images which are …

Abstract Face Super-Resolution (SR) is a domain-specific superresolution problem. The
facial prior knowledge can be leveraged to better super-resolve face images. We present a …

General image super-resolution techniques have difficulties in recovering detailed face
structures when applying to low resolution face images. Recent deep learning based …

Most modern face super-resolution methods resort to convolutional neural networks (CNN)
to infer high-resolution (HR) face images. When dealing with very low resolution (LR) …

Face alignment, which fits a face model to an image and extracts the semantic meanings of
facial pixels, has been an important topic in the computer vision community. However, most …

This paper addresses 2 challenging tasks: improving the quality of low resolution facial
images and accurately locating the facial landmarks on such poor resolution images. To this …

Recent works based on deep learning and facial priors have succeeded in super-resolving
severely degraded facial images. However, the prior knowledge is not fully exploited in …

Single-image super-resolution is of great importance for vision applications, and numerous
algorithms have been proposed in recent years. Despite the demonstrated success, these …