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 …

Deep neural networks have attained great success in handling high dimensional data,
especially images. However, generating naturalistic images containing ginormous subjects …

Existing face restoration researches typically rely on either the image degradation prior or
explicit guidance labels for training, which often lead to limited generalization ability over …

In recent years, deep convolutional neural networks (CNNs) have been widely employed in
image super-resolution. Thanks to the power of deep CNNs, the reconstruction performance …

Real-world single image Super-Resolution (SR) aims to enhance the resolution and
reconstruct High-Resolution (HR) details of real Low-Resolution (LR) images. This is …

Practical systems such as in surveillance applications capture Low-Resolution (LR) face
images due to the wider angle of imaging or longer stand-off distance to the camera …

In this research work, a new Image super-resolution-based Face Emotion Recognition
Model has been introduced. The proposed work includes two major phases:(a) Facial image …

In recent years, with the increasing power of computers and Graphics Processing Units
(GPUs), vast variety of deep neural networks architectures have been created and realized …

Face restoration is an inherently ill-posed problem, where additional prior constraints are
typically considered crucial for mitigating such pathology. However, real-world image prior …

Applications that involve supervised training require paired images. Researchers of single
image super-resolution (SISR) create such images by artificially generating blurry input …