Face hallucination in low-light environments is an extremely challenging task due to the
significant loss of facial structure and facial texture information. Although cascading image …

Face super-resolution (SR) algorithms have recently made significant progress. However,
most existing methods prefer to employ texture and structure information together to promote …

Face Super-Resolution (FSR) plays a crucial role in enhancing low-resolution face images,
which is essential for various face-related tasks. However, FSR may alter individuals' …

Deep convolutional neural networks have demonstrated significant performance
improvements in face super-resolution tasks. However, many deep learning-based …

Deep learning techniques in electronic surveillance have shown impressive performance for
super-resolution (SR) of captured low-quality face images. Most of these techniques adopt …

ABSTRACT Face Super-Resolution (FSR) represents a significant branch of image super-
resolution, aiming to reconstruct low-resolution face images into high-resolution …

Face super-resolution technology can significantly enhance the resolution and quality of
face images, which is crucial for applications such as surveillance, forensics, and face …

Face Super-resolution (FSR) models encounter a significant challenge related to extremely
low-dimensional (pixels) and degraded input images. This deficiency in crucial facial details …

Face super-resolution generates high-resolution face images from low-resolution inputs,
supporting face recognition in challenging environments. While deep learning methods, like …

The purpose of face super-resolution (FSR) is to reconstruct high-resolution (HR) face
images from low-resolution (LR) inputs. With the continuous advancement of deep learning …