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Chaofeng Chen

The University of Hong Kong

12 papers hereh-index 152.2k citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author7

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV12
affiliations
  • The University of Hong Kong
Homepage
same name
  • Chaofeng Chen — 17 papers, h 18
  • Chaofeng Chen — 6 papers, h 4
  • Chaofeng Chen — 6 papers, h 2
  • Chaofeng Chen — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedLearning Spatial Attention for Face Super-Resolution

219 citations · 265 across the 10 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 219 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…

cs.CV2020★ 14 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.…

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