Citation

Lew, Kai Liang and Sim, Kok Swee and Tan, Shing Chiang (2023) SEM Image Deep Learning Noise Level Classification. In: 1st FET PG Engineering Colloquium Proceedings 2023, 16 June - 15 July 2023, Multimedia University, Malaysia. (Submitted)

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Abstract

Scanning Electron Microscopy (SEM) is a type of electron microscope that allows for high-resolution imaging of surface structures and composition at the nanoscale. However, Gaussian noise can significantly impact image quality and make it difficult to accurately interpret and analyze images. To address this issue, classical image filters such as median and Gaussian filters can be used, but selecting a filter and its parameters can be challenging. This paper proposes a deep learning approach to classify the noise level in SEM images that have been corrupted with Gaussian noise.

Item Type:	Conference or Workshop Item (Poster)
Subjects:	Q Science > QC Physics > QC350-467 Optics. Light
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	15 Aug 2023 01:31
Last Modified:	15 Aug 2023 01:31
URII:	http://shdl.mmu.edu.my/id/eprint/11617

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