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Chao Huang

12 papers hereh-index 8263 citations28 works total

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

author position
  • first author3
  • middle author8

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV3
  • math.NA2
  • cs.CE1
  • cs.DM1
  • math.OC1
same name
  • Chao Huang — 44 papers, h 34
  • Chao Huang — 24 papers, h 11
  • Chao Huang — 18 papers, h 16
  • Chao Huang — 16 papers, h 29
  • Chao Huang — 16 papers, h 14
  • Chao Huang — 14 papers, h 30

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
20222026
collaborators
Showing 2024Show all

4 papers · 1 filter

math.OC2024

Inertial Proximal Difference-of-Convex Algorithm with Convergent Bregman Plug-and-Play for Nonconvex Imaging

Tsz Ching Chow, Chaoyan Huang, Zhongming Wu +2

Imaging tasks are typically tackled using a structured optimization framework. This paper delves into a class of algorithms for difference-of-convex (DC) structured optimization, f…

math.NA2024

Deep Block Proximal Linearised Minimisation Algorithm for Non-convex Inverse Problems

Chaoyan Huang, Zhongming Wu, Yanqi Cheng +3

Image restoration is typically addressed through non-convex inverse problems, which are often solved using first-order block-wise splitting methods. In this paper, we consider a ge…

math.NA2024

Extrapolated Plug-and-Play Three-Operator Splitting Methods for Nonconvex Optimization with Applications to Image Restoration

Zhongming Wu, Chaoyan Huang, Tieyong Zeng

This paper investigates the convergence properties and applications of the three-operator splitting method, also known as Davis-Yin splitting (DYS) method, integrated with extrapol…

cs.CV2024

Edge-guided Low-light Image Enhancement with Inertial Bregman Alternating Linearized Minimization

Chaoyan Huang, Zhongming Wu, Tieyong Zeng

Prior-based methods for low-light image enhancement often face challenges in extracting available prior information from dim images. To overcome this limitation, we introduce a sim…

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