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Chunyan Shan

dblp:166/2512 · DBLP profile ↗
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4ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 3Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 77% Image and video processing · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › microscopy imaging › computational microscopy
structured illumination microscopy
0.312018
A Frequency Domain SIM Reconstruction Algorithm Using Reduced Number of Images · IEEE Trans. Image Process. 2018
Image and video processing
super-resolution
0.112018
A Frequency Domain SIM Reconstruction Algorithm Using Reduced Number of Images · IEEE Trans. Image Process. 2018

Methods — techniques the papers use, named apart from their topics

ordinary least squares · 0.3frequency-domain reconstruction · 0.3
YearPublicationVenuePosition
2025 MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification
Yanbei Liu, Fang Zhang 0001, Lei Geng, Chunyan Shan, Xiangyu Cao, Zhitao Xiao
Medical Image Anal.5
2018 A Frequency Domain SIM Reconstruction Algorithm Using Reduced Number of Images
abstract
Conventional two-dimensional structured illumination microscopy (SIM) requires 9 raw images to reconstruct a super-resolved image. In order to increase the frame rate of 2DSIM, attempts are being made to reduce the number of raw SIM images. However, all the proposed SIM reconstruction algorithms (SIM-RA) capable of reconstructing super-resolution (SR) image with a reduced number of raw SIM images operate in the spatial domain. Here, we present a frequency domain SIM-RA based on ordinary least squares technique, which enables reconstruction of SR image using 4 raw SIM images. Unlike the spatial domain RA, which produces the SR image through iterative convergence, the presented RA provides a single step solution. It also reveals the fundamental limitation of least number of raw images required for resolution doubling in SIM.
Amit Lal, Chunyan Shan, Xiaoshuai Huang, Weijian Zong, Liangyi Chen, Peng Xi
IEEE Trans. Image Process.2
2015 Hard Exudates Detection Method Based on Background-Estimation
Zhitao Xiao, Lei Geng, Fang Zhang 0001, Jun Wu 0014, Long Su, Chunyan Shan, Yuling Sun, Yu Xiao 0001, Weiqiang Du
ICIG (2)8
2015 Using Phase Congruency Model for Microaneurysms Detection in Fundus Image
Zhitao Xiao, Fang Zhang 0001, Lei Geng, Jun Wu 0014, Long Su, Chunyan Shan
ICPRAM (2)7