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 …

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 …

Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations-
small sensor size, compact lenses and the lack of specific hardware,-impede them to …

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 …

Conventional face super-resolution methods, also known as face hallucination, are limited
up to 2\! ∼\! 4 * 2∼ 4× scaling factors where 4 ∼ 16 4∼ 16 additional pixels are estimated …

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 …

Face hallucination is a domain-specific super-resolution problem with the goal to generate
high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing …

We present a novel framework for hallucinating faces of unconstrained poses and with very
low resolution (face size as small as 5pxIOD). In contrast to existing studies that mostly …

Low-end and compact mobile cameras demonstrate limited photo quality mainly due to
space, hardware and budget constraints. In this work, we propose a deep learning solution …

Along with the performance improvement of deep-learning-based face hallucination
methods, various face priors (facial shape, facial landmark heatmaps, or parsing maps) have …