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Q. Liao

23 papers hereh-index 366.5k citations357 works total

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

author position
  • middle author8
  • last author12

Across the 20 of 23 papers where every author was matched, so the position is known.

fields
  • cs.CV19
  • eess.IV3
  • cs.LG1
same name
  • Q. Liao — 39 papers, h 42
  • Q. Liao — 17 papers, h 24
  • Q. Liao — 8 papers, h 14
  • Q. Liao — 6 papers, h 8
  • Q. Liao — 5 papers, h 3
  • Q. Liao — 5 papers, h 2

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
20182023
most citedLightweight Feature Fusion Network for Single Image Super-Resolution

58 citations · 132 across the 13 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019

An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion

Haoyu Ma, Qingmin Liao, Juncheng Zhang +2

Capturing an all-in-focus image with a single camera is difficult since the depth of field of the camera is usually limited. An alternative method to obtain the all-in-focus image…

eess.IV2019

LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution

Wenming Yang, Xuechen Zhang, Yapeng Tian +3

In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution. Compared with t…

cs.CV2019

Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network

Haoyu Ma, Juncheng Zhang, Shaojun Liu +1

Since it is usually difficult to capture an all-in-focus image of a 3D scene directly, various multi-focus image fusion methods are employed to generate it from several images focu…

cs.CV2019★ 58 cited

Lightweight Feature Fusion Network for Single Image Super-Resolution

Wenming Yang, Wei Wang, Xuechen Zhang +2

Single image super-resolution(SISR) has witnessed great progress as convolutional neural network(CNN) gets deeper and wider. However, enormous parameters hinder its application to…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.