Citation

Lew, Kai Liang and Sim, Kok Swee and Tan, Shing Chiang (2023) SEM Image Deep Learning Noise Level Classification. In: 2nd FET PG Engineering Colloquium Proceedings 2023, 1-31 December 2023, Multimedia University, Malaysia. (Submitted)

Text 22. PPM Poster - Lew Kai Liang - Chan Wai Ti.pdf - Submitted Version Restricted to Registered users only Download (360kB)

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)
Uncontrolled Keywords:	Electron Microscopy (SEM), Deep Learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	03 Apr 2024 02:55
Last Modified:	03 Apr 2024 02:55
URII:	http://shdl.mmu.edu.my/id/eprint/12358

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