人工智能
光学
噪音(视频)
小波
材料科学
预处理器
分辨率(逻辑)
计算机视觉
计算机科学
模式识别(心理学)
物理
图像(数学)
作者
Zina-Sabrina Duma,Tuomas Sihvonen,Jouni Havukainen,Вилле Рейникайнен,Сату-Пиа Рейникайнен
出处
期刊:Micron
[Elsevier]
日期:2022-12-01
卷期号:163: 103361-103361
被引量:6
标识
DOI:10.1016/j.micron.2022.103361
摘要
Fusion and quality enhancement of the low-resolution Energy Dispersive X-ray Spectroscopy (EDS) maps to Scanning Electron Microscopy (SEM) panchromatic images has been proven effective by various pansharpening algorithms. The present paper aims to target the preprocessing of these maps to enhance the efficiency of the pansharpening process, with as little information loss on the chemical distribution, and as little propagated noise as possible. EDS maps present different noise intensities depending on the flatness of the surface of the analyzed object. The uneven surface maps have limited analytical value due to the noise and have not been resolution-enhanced with pansharpening due to the noise propagation limitation. In this paper, different preprocessing methods are evaluated for enabling uneven-surface particles to pansharpening: background removal, upsampling, and noise filtering. The sequence of applying preprocessing steps is analyzed. The optimal order of preprocessing steps is (i) background removal, (ii) noise filtering, and (iii) interpolation. A methodology for each of these steps is presented in the paper. The best performing pansharpening methodology is chosen to be Affinity for individual map analysis and Wavelet for multi-elemental fusion purposes. Following the methodology results in high-resolution EDS maps, even for uneven-surface particles which are, for the first time in literature, subjected to pansharpening.
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