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20182022
most citedLearning Spatial Attention for Face Super-Resolution

219 citations · 245 across the 7 of their papers we have counts for

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9 papers · 1 filter

cs.CV202211 cited

S-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

Wenqi Yang, Guanying Chen, Chaofeng Chen +2

In this paper, we address the "dual problem" of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene…

cs.CV20221 cited

From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution

Xiaoming Li, Chaofeng Chen, Xianhui Lin +2

How to design proper training pairs is critical for super-resolving real-world low-quality (LQ) images, which suffers from the difficulties in either acquiring paired ground-truth…

cs.CV2022

A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification

Shaozhe Hao, Chaofeng Chen, Zhenfang Chen +1

Face recognition under ideal conditions is now considered a well-solved problem with advances in deep learning. Recognizing faces under occlusion, however, still remains a challeng…

cs.CV2020219 cited

Learning Spatial Attention for Face Super-Resolution

Chaofeng Chen, Dihong Gong, Hao Wang +2

General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods t…

cs.CV2020

Face Sketch Synthesis with Style Transfer using Pyramid Column Feature

Chaofeng Chen, Xiao Tan, Kwan-Yee K. Wong

In this paper, we propose a novel framework based on deep neural networks for face sketch synthesis from a photo. Imitating the process of how artists draw sketches, our framework…

cs.CV2020

Progressive Semantic-Aware Style Transformation for Blind Face Restoration

Chaofeng Chen, Xiaoming Li, Lingbo Yang +3

Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) resu…