activity
20142020
most citedConvolutional Network for Attribute-driven and Identity-preserving Human Face Generation

48 citations · 187 across the 17 of their papers we have counts for

collaborators

17 papers

cs.CV202020 cited

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Pengxu Wei, Hannan Lu, Radu Timofte +68

This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…

cs.CV20203 cited

Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision

Yuxiang Wei, Ming Liu, Haolin Wang +3

Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pos…

eess.IV20203 cited

Unpaired Learning of Deep Image Denoising

Xiaohe Wu, Ming Liu, Yue Cao +2

We investigate the task of learning blind image denoising networks from an unpaired set of clean and noisy images. Such problem setting generally is practical and valuable consider…

cs.CV20209 cited

Component Divide-and-Conquer for Real-World Image Super-Resolution

Pengxu Wei, Ziwei Xie, Hannan Lu +4

In this paper, we present a large-scale Diverse Real-world image Super-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquer Super-Resolution (SR) network, exploring t…

cs.CV202014 cited

Blind Face Restoration via Deep Multi-scale Component Dictionaries

Xiaoming Li, Chaofeng Chen, Shangchen Zhou +3

Recent reference-based face restoration methods have received considerable attention due to their great capability in recovering high-frequency details on real low-quality images.…

eess.IV20202 cited

Lightweight image super-resolution with enhanced CNN

Chunwei Tian, Ruibin Zhuge, Zhihao Wu +4

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amo…