The goal of learning-based image super resolution (SR) is to generate a plausible and
visually pleasing high-resolution (HR) image from a given low-resolution (LR) input. The SR …

The performance of traditional face recognition systems is sharply reduced when
encountered with a low-resolution (LR) probe face image. To obtain much more detailed …

Although research on detection of saliency and visual attention has been active over recent
years, most of the existing work focuses on still image rather than video based saliency. In …

Face image super-resolution has attracted much attention in recent years. Many algorithms
have been proposed. Among them, sparse representation (SR)-based face image super …

In human-machine interaction, human face is one of the core factors. However, due to the
limitations of imaging conditions and low-cost imaging sensors, the captured faces are often …

Face image super-resolution (FSR) algorithms are capable of providing a high-resolution
image from an input low-resolution (LR) image. Various FSR algorithms use a set of training …

In recent years face hallucination or super-resolution (SR) is getting much attention due to its
wide applicability in real world scenarios. The existing SR methods and models perform well …

In recent years, face hallucination (FH) techniques are developed to generate the high-
resolution (HR) version of the captured blurry low-resolution face images. The popular FH …

With the popularity of high-efficiency video coding (HEVC) standard, a video server usually
transcodes a video stream to HEVC for its higher compression ratio. In this paper, a fast H …

Recently, the locality linear coding (LLC) has attracted more and more attentions in the
areas of image processing and computer vision. However, the conventional LLC with real …