Contrast enhancement of scanning electron microscopy images using a noncomplex multiphase algorithm

• Autorzy: Alsaygh Zaid , Al-Ameen Zohair

Identyfikatory

DOI

10.35784/acs-2022-11

• Języki publikacji: EN

Abstrakty

EN

Microscopic technology has recently flourished, allowing unparalleled viewing of microscopic elements invisible to the normal eye. Still, the existence of unavoidable constraints led on many occasions to have low contrast scanning electron microscopic (SEM) images. Thus, a noncomplex multiphase (NM) algorithm is proposed in this study to provide better contrast for various SEM images. The developed algorithm contains the following stages: first, the intensities of the degraded image are modified using a two-step regularization procedure. Next, a gamma-corrected cumulative distribution function of the logarithmic uniform distribution approach is applied for contrast enhancement. Finally, an automated histogram expansion technique is used to redistribute the pixels of the image properly. The NM algorithm is applied to natural-contrast distorted SEM images, as well as its results are compared with six algorithms with different processing notions. To assess the quality of images, three modern metrics are utilized, in that each metric measures the quality based on unique aspects. Extensive appraisals revealed the adequate processing abilities of the NM algorithm, as it can process many images suitably and its performances outperformed many available contrast enhancement algorithms in different aspects.

Słowa kluczowe

EN

image enhancement; SEM images; statistical approaches; contrast enhancement

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2022
• Tom: Vol. 18, no 2
• Strony: 28--42
• Opis fizyczny: Bibliogr. 33 poz., fig., tab.

Twórcy

autor

Alsaygh Zaid

• Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq

autor

Al-Ameen Zohair

• Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq

• https://orcid.org/0000-0003-3630-2134

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).