42 citations · 168 across the 15 of their papers we have counts for
16 papers
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution
Xinyue Wei, Haozhi Huang, Yujin Shi +3
The classic Monte Carlo path tracing can achieve high quality rendering at the cost of heavy computation. Recent works make use of deep neural networks to accelerate this process,…
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data
Ru Li, Chuan Wang, Jue Wang +4
The paper proposes a method to effectively fuse multi-exposure inputs and generate high-quality high dynamic range (HDR) images with unpaired datasets. Deep learning-based HDR imag…
DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation
Haipeng Li, Shuaicheng Liu, Jue Wang
Mobile captured images can be aligned using their gyroscope sensors. Optical image stabilizer (OIS) terminates this possibility by adjusting the images during the capturing. In thi…
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
Kunming Luo, Chuan Wang, Shuaicheng Liu +3
We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tac…
Practical Deep Raw Image Denoising on Mobile Devices
Yuzhi Wang, Haibin Huang, Qin Xu +3
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks ar…
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
Kunming Luo, Chuan Wang, Nianjin Ye +2
Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove the…