SEM Image Quality Assessment Based on Texture Inpainting DOI Open Access
Zhaolin Lu, Ziyan Zhang, Yi Wang

et al.

IEICE Transactions on Information and Systems, Journal Year: 2021, Volume and Issue: E104.D(2), P. 341 - 345

Published: Jan. 31, 2021

This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that information of SEM is quite sensitive to distortions, a inpainting network first trained extract features. Then weights are transferred IQA help it learn effective representation distorted image. Finally, supervised fine-tuning conducted predict score. Experimental results dataset demonstrate advantages presented method.

Language: Английский

A novel hybrid cryptosystem based on DQFrFT watermarking and 3D-CLM encryption for healthcare services DOI
Fatma Khallaf, Walid El‐Shafai, El‐Sayed M. El‐Rabaie

et al.

Frontiers of Information Technology & Electronic Engineering, Journal Year: 2023, Volume and Issue: 24(7), P. 1045 - 1061

Published: July 1, 2023

Language: Английский

Citations

4
Image quality evaluation for FIB‐SEM images DOI Creative Commons
Diego Roldán, Claudia Redenbach, Katja Schladitz

et al.

Journal of Microscopy, Journal Year: 2023, Volume and Issue: 293(2), P. 98 - 117

Published: Dec. 19, 2023

Focused ion beam scanning electron microscopy (FIB-SEM) tomography is a serial sectioning technique where an FIB mills off slices from the material sample that being analysed. After every slicing, SEM image taken showing newly exposed layer of sample. By combining all in stack, 3D generated. However, specific artefacts caused by imaging distort images, hampering morphological analysis structure. Typical quality problems are noise and lack contrast or focus. Moreover, milling, namely, curtaining charging artefacts. We propose indices for evaluation FIB-SEM data sets. The validated on real experimental different structures materials.

Language: Английский

Citations

2
SEM Image Quality Assessment Based on Intuitive Morphology and Deep Semantic Features DOI Creative Commons
Haoran Wang, Shiyin Li, Jicun Ding

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 111377 - 111388

Published: Jan. 1, 2022

The widespread use of scanning electron microscopy (SEM) has increased the requirements for SEM image quality. images obtained by beam feedback have more complex texture features than natural optical imaging, and this condition results in poor performance algorithms used assessing quality on datasets,meanwhile,the field assessment(IQA) is mostly aimed at specific distortion types. In order to solve above two problems,to address rich texture, few edges, extreme sensitivity degree images, we propose a semantic IQA (TSIQA) method based sparse mask information entropy increase. First, construct neural network containing module (SMM), which extract intuitive spatial channel domains. Simultaneously, growth attention (IGA) introduced into SMM detect difference between current past extracting deep information. assessment experiments datasets show that compared with state-of-the-art methods, including popular no-reference techniques adapted SEM-IQA, TSIQA superiority typical criteria.

Language: Английский

Citations

2
SEM Image Quality Assessment Based on Texture Inpainting DOI Open Access
Zhaolin Lu, Ziyan Zhang, Yi Wang

et al.

IEICE Transactions on Information and Systems, Journal Year: 2021, Volume and Issue: E104.D(2), P. 341 - 345

Published: Jan. 31, 2021

This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that information of SEM is quite sensitive to distortions, a inpainting network first trained extract features. Then weights are transferred IQA help it learn effective representation distorted image. Finally, supervised fine-tuning conducted predict score. Experimental results dataset demonstrate advantages presented method.

Language: Английский

Citations

2