Neural-network-based single image depth prediction (SIDP) is a challengi...
We introduce VA-DepthNet, a simple, effective, and accurate deep neural
...
Measuring perceptual color differences (CDs) is of great importance in m...
Channel (or 3D filter) pruning serves as an effective way to accelerate ...
In this paper, we summarize the 1st NTIRE challenge on stereo image
supe...
Generative adversarial networks (GANs) have promoted remarkable advances...
Exploiting the relationships between attributes is a key challenge for
i...
Video super-resolution has recently become one of the most important
mob...
Kernel estimation is generally one of the key problems for blind image
s...
This paper reviews the video extreme super-resolution challenge associat...
In the learning based video compression approaches, it is an essential i...
Most video super-resolution methods super-resolve a single reference fra...
In this paper, we study two challenging and less-touched problems in sin...
In this paper, we tackle the problem of convolutional neural network des...
We propose the first practical multitask image enhancement network, that...
Existing unsupervised video-to-video translation methods fail to produce...
These days, unsupervised super-resolution (SR) has been soaring due to i...
Network pruning has been the driving force for the efficient inference o...
In this paper, we analyze two popular network compression techniques, i....
This paper reviews the AIM 2019 challenge on real world super-resolution...
Most of the recent literature on image super-resolution (SR) assumes the...
This paper reviews the AIM 2019 challenge on constrained example-based s...
Convolutional neural networks (CNNs) based solutions have achieved
state...
Nowadays, due to the ubiquitous visual media there are vast amounts of
a...
Learning-based lossy image compression usually involves the joint
optimi...
This paper reviews the first challenge on efficient perceptual image
enh...
Tremendous advances in image restoration tasks such as denoising and
sup...
Arithmetic encoding is an essential class of coding techniques which hav...
Model-based optimization methods and discriminative learning methods hav...
Lossy image compression is generally formulated as a joint rate-distorti...
Low rank matrix approximation (LRMA), which aims to recover the underlyi...
In recent years, the nuclear norm minimization (NNM) problem has been
at...