1.2k citations · 2.5k across the 68 of their papers we have counts for
6 papers · 1 filter
Deep Network Interpolation for Continuous Imagery Effect Transition
Xintao Wang, Ke Yu, Chao Dong +2
Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect. However, the large variety of user flavors mot…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Xintao Wang, Ke Yu, Shixiang Wu +6
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the ha…
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Yuan Yuan, Siyuan Liu, Jiawei Zhang +3
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from tradi…
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
Ke Yu, Chao Dong, Liang Lin +1
We investigate a novel approach for image restoration by reinforcement learning. Unlike existing studies that mostly train a single large network for a specialized task, we prepare…
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
Xintao Wang, Ke Yu, Chao Dong +1
Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic tex…