GAN-Based Method for Synthesizing Multi-focus Cell Images

Ken’ich Morooka, Xueru Zhang, Shoko Miyauchi, Ryo Kurazume, Eiji Ohno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


This paper presents a method for synthesizing multi-focus cell images by using generative adversarial networks (GANs). The proposed method, called multi-focus image GAN (MI-GAN), consists of two generators. A base image generator synthesizes a 2D base cell image from random noise. Using the generated base image, a multi-focus cell image generator produces 11 realistic multi-focus images of the cell while considering the relationships between the images acquired at successive focus points. From experimental results, MI-GAN achieves the good performance to generate realistic multi-focus cell images.

Original languageEnglish
Title of host publicationImage and Video Technology - PSIVT 2019 International Workshops, Revised Selected Papers
EditorsJoel Janek Dabrowski, Ashfaqur Rahman, Manoranjan Paul
Number of pages8
ISBN (Print)9783030397692
Publication statusPublished - Jan 1 2020
Event9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019 - Sydney, Australia
Duration: Nov 18 2019Nov 22 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11994 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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