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 …

Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the
reconstruction of face images. The main challenge of face SR is to restore essential facial …

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods
have achieved great progress in restoring degraded facial details by joint training with facial …

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the
low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved …

Recently, facial priors (eg, facial parsing maps and facial landmarks) have been widely
employed in prior-guided face super-resolution (FSR) because it provides the location of …

State-of-the-art face super-resolution methods employ deep convolutional neural networks
to learn a mapping between low-and high-resolution facial patterns by exploring local …

Nowadays, due to the ubiquitous visual media there are vast amounts of already available
high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution …

State-of-the-art face super-resolution methods use deep convolutional neural networks to
learn a mapping between low-resolution (LR) facial patterns and their corresponding high …

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 …