Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its
introduction is the unmatched resolution in the lateral and axial directions (ie, resolution …

This paper proposes a fast and reliable method for anomaly detection and localization in
video data showing crowded scenes. Time-efficient anomaly localization is an ongoing …

Convolutional neural networks have been proven to be of great benefit for single-image
super-resolution (SISR). However, previous works do not make full use of multi-scale …

Recently, consumer depth cameras have gained significant popularity due to their affordable
cost. However, the limited resolution and the quality of the depth map generated by these …

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 …

Single image superresolution is a classic and active image processing problem, which aims
to generate a high-resolution (HR) image from a low-resolution input image. Due to the …

Generating images via a generative adversarial network (GAN) has attracted much attention
recently. However, most of the existing GAN-based methods can only produce lowresolution …

Stereo super-resolution (SR) aims to enhance the spatial resolution of one camera view
using additional information from the other. Previous deep-learning-based stereo SR …

Depth image super-resolution is an extremely challenging task due to the information loss in
sub-sampling. Deep convolutional neural network has been widely applied to color image …