The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …

Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of
leveraging traditional low-level or handcrafted image priors as the restoration constraints …

Recent research on super-resolution has achieved greatsuccess due to the development of
deep convolutional neu-ral networks (DCNNs). However, super-resolution of arbi-trary scale …

Many methods have set state-of-the-art performance on restoring images degraded by bad
weather such as rain, haze, fog, and snow, however they are designed specifically to handle …

We introduce a new generator architecture, aimed at fast and efficient high-resolution image-
to-image translation. We design the generator to be an extremely lightweight function of the …

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …

To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse
coding (DCSC) network architecture. We design our DCSC in the framework of classic …

Point cloud upsampling is vital for the quality of the mesh in three-dimensional
reconstruction. Recent research on point cloud upsampling has achieved great success due …

We propose a general dual convolutional neural network (DualCNN) for low-level vision
problems, eg, super-resolution, edge-preserving filtering, deraining, and dehazing. These …