67 citations · 88 across the 7 of their papers we have counts for
7 papers
Toward Tiny and High-quality Facial Makeup with Data Amplify Learning
Qiaoqiao Jin, Xuanhong Chen, Meiguang Jin +5
Contemporary makeup approaches primarily hinge on unpaired learning paradigms, yet they grapple with the challenges of inaccurate supervision (e.g., face misalignment) and sophisti…
Lightweight network towards real-time image denoising on mobile devices
Zhuoqun Liu, Meiguang Jin, Ying Chen +3
Deep convolutional neural networks have achieved great progress in image denoising tasks. However, their complicated architectures and heavy computational cost hinder their deploym…
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement
Canqian Yang, Meiguang Jin, Yi Xu +3
Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms. However, they embed…
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li, Kai Zhang, Radu Timofte +108
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement
Canqian Yang, Meiguang Jin, Xu Jia +2
The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it into a discretiz…
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
Meiguang Jin, Givi Meishvili, Paolo Favaro
We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulat…
Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli, Meiguang Jin, Paolo Favaro +1
In this paper we introduce a natural image prior that directly represents a Gaussian-smoothed version of the natural image distribution. We include our prior in a formulation of im…