Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning

Eran Gur; Daniel Havivi; Samer Zahaykeh

doi:10.1007/978-3-032-15632-7_32

Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning

Eran Gur
, Daniel Havivi
, Samer Zahaykeh

Azrieli College of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In many imaging systems the output is a noisy version of the required image due to inaccuracies in the setup or low-quality equipment. Obtaining the clean high-resolution image from its noisy version is a problem that many image processing setups have tried to deal with, some more successfully and others less. In this work the authors suggest using a large data set of images for which we know the original undamaged image, build an optimal reconstruction filter for each one using iterative methods and use this data base of filters to generate a fuzzy logic inference engine that will welcome a new unknown noisy image and by comparing it to the database generate a single reconstruction filter to denoise that image. The authors present the process of building the database and the final denoising filter and show excellent correlation results in terms of image reconstruction.

Original language	English
Title of host publication	Computational Intelligence - 17th International Joint Conference, IJCCI 2025, Proceedings
Editors	Francesco Marcelloni, Kurosh Madani, Niki van Stein, Joaquim Filipe
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	568-575
Number of pages	8
ISBN (Print)	9783032156310
DOIs	https://doi.org/10.1007/978-3-032-15632-7_32
State	Published - 2026
Externally published	Yes
Event	17th International Joint Conference on Computational Intelligence, IJCCI 2025 - Marbella, Spain Duration: 22 Oct 2025 → 24 Oct 2025

Publication series

Name	Communications in Computer and Information Science
Volume	2827 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	17th International Joint Conference on Computational Intelligence, IJCCI 2025
Country/Territory	Spain
City	Marbella
Period	22/10/25 → 24/10/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Fourier Filtering
Fuzzy Logic
Phase Only Filters
Phase Retrieval

Access to Document

10.1007/978-3-032-15632-7_32

Cite this

Gur, E., Havivi, D., & Zahaykeh, S. (2026). Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning. In F. Marcelloni, K. Madani, N. van Stein, & J. Filipe (Eds.), Computational Intelligence - 17th International Joint Conference, IJCCI 2025, Proceedings (pp. 568-575). (Communications in Computer and Information Science; Vol. 2827 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-15632-7_32

Gur, Eran ; Havivi, Daniel ; Zahaykeh, Samer. / Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning. Computational Intelligence - 17th International Joint Conference, IJCCI 2025, Proceedings. editor / Francesco Marcelloni ; Kurosh Madani ; Niki van Stein ; Joaquim Filipe. Springer Science and Business Media Deutschland GmbH, 2026. pp. 568-575 (Communications in Computer and Information Science).

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abstract = "In many imaging systems the output is a noisy version of the required image due to inaccuracies in the setup or low-quality equipment. Obtaining the clean high-resolution image from its noisy version is a problem that many image processing setups have tried to deal with, some more successfully and others less. In this work the authors suggest using a large data set of images for which we know the original undamaged image, build an optimal reconstruction filter for each one using iterative methods and use this data base of filters to generate a fuzzy logic inference engine that will welcome a new unknown noisy image and by comparing it to the database generate a single reconstruction filter to denoise that image. The authors present the process of building the database and the final denoising filter and show excellent correlation results in terms of image reconstruction.",

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Gur, E, Havivi, D & Zahaykeh, S 2026, Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning. in F Marcelloni, K Madani, N van Stein & J Filipe (eds), Computational Intelligence - 17th International Joint Conference, IJCCI 2025, Proceedings. Communications in Computer and Information Science, vol. 2827 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 568-575, 17th International Joint Conference on Computational Intelligence, IJCCI 2025, Marbella, Spain, 22/10/25. https://doi.org/10.1007/978-3-032-15632-7_32

Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning. / Gur, Eran; Havivi, Daniel; Zahaykeh, Samer.
Computational Intelligence - 17th International Joint Conference, IJCCI 2025, Proceedings. ed. / Francesco Marcelloni; Kurosh Madani; Niki van Stein; Joaquim Filipe. Springer Science and Business Media Deutschland GmbH, 2026. p. 568-575 (Communications in Computer and Information Science; Vol. 2827 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2026

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Gur E, Havivi D, Zahaykeh S. Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning. In Marcelloni F, Madani K, van Stein N, Filipe J, editors, Computational Intelligence - 17th International Joint Conference, IJCCI 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. 2026. p. 568-575. (Communications in Computer and Information Science). doi: 10.1007/978-3-032-15632-7_32

Image Denoising Using a Gerchberg-Saxton Based Data-Set and Fuzzy Logic Reasoning

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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