Face frontalization refers to the process of synthesizing the frontal view of a face from a
given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely …

In single image super-resolution (SISR), given a low-resolution (LR) image, one wishes to
find a high-resolution (HR) version of it which is both accurate and photorealistic. Recently, it …

Single image deraining aims to remove rain perturbation while restoring the clean
background scene from a rain image. However, existing methods tend to produce blurry and …

Though generative adversarial networks (GANs) can hallucinate high-quality high-resolution
(HR) faces from low-resolution (LR) faces, they cannot ensure identity preservation during …

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 …

Deep neural networks (DNNs) have witnessed remarkable achievement in image super-
resolution (SR), and plenty of DNN-based SR models with elaborated network designs have …

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 …

Deep learning-based super-resolution (SR) techniques have generally achieved excellent
performance in the computer vision field. Recently, it has been proven that three …

We present a novel method for predicting accurate depths from monocular images with high
efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is …

Although remarkable progress has been made on single-image super-resolution (SISR),
deep learning methods cannot be easily applied to real-world applications due to the …