A fast and efficient numerical algorithm for image segmentation and denoising

Yuzi Jin, Soobin Kwak, Seokjun Ham, Junseok Kim

Research output: Contribution to journalArticlepeer-review


Image segmentation is the process of partitioning an image into homogenous regions, and represents one of the most fundamental and important procedures in image processing. Image denoising is a process to remove unwanted noise from a digital image, enhancing its visual quality. Various algorithms, like non-local means and deep learning-based approaches, have been developed to remove noise while preserving important image details. Currently, the prevalent application of pattern recognition technology is achieved through the implementation of image segmentation algorithms. In this study, we present a new, highly efficient, and fast computational scheme specifically developed for a phase-field mathematical model of image segmentation. The numerical methodology is based on an operator splitting method (OSM). The split operators are solved by using closed-form analytic solutions and a finite difference method (FDM) with an alternating direction explicit (ADE) method. To show the notable efficiency and rapid computational performance of the proposed computational algorithm, we conduct a series of numerical experiments. Through these computational tests, we confirm a significant contribution to the advancement of methodologies employed in the critical domain of image processing.

Original languageEnglish
Pages (from-to)5015-5027
Number of pages13
JournalAIMS Mathematics
Issue number2
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 the Author(s).


  • alternating direction explicit method
  • image segmentation
  • phase-field model

ASJC Scopus subject areas

  • General Mathematics


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