A Hybrid Supervised Fusion Deep Learning Framework for Microscope Multi-Focus Images

Qiuhui Yang, Hao Chen, Mingfeng Jiang, Mingwei Wang, Jiong Zhang, Yue Sun, Tao Tan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The quality of multi-focus microscopic image fusion hinges upon the precision of the image registration technology. However, algorithms for registration tailored specifically for multifocal microscopic images are lacking. Due to the presence of fuzzy regions and weak textures of multi-focus microscope images, the registration of patches is suboptimal. For these problems, this paper formulates a hybrid supervised deep learning model. It can improve the accuracy of registration and fusion. The generalization ability of the model to the actual deformation field enhance by the artificial deformation field. A step of patch movement simulation is employed to blur the multi-focus microscopic images and make synthetic flow, thus emulating distinct fuzzy regions in the two images to be registered, consequently enhancing the model's generalization ability. The experiments demonstrate that our proposed approach is superior to the existing registration algorithms and improves the accuracy of image fusion.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings
EditorsBin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031500770
Publication statusPublished - 2024
Event40th Computer Graphics International Conference, CGI 2023 - Shanghai, China
Duration: 28 Aug 20231 Sept 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14498 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference40th Computer Graphics International Conference, CGI 2023


  • Fusion
  • Multi-focus microscope images
  • Supervised registration


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