Paper Info
Title
SEM Image Quality Assessment Based on Intuitive Morphology and Deep Semantic Features
Abstract
The widespread use of scanning electron microscopy (SEM) has increased the requirements for SEM image quality. SEM images obtained by electron beam feedback have more complex texture features than natural images obtained by optical imaging, and this condition results in poor performance of algorithms used for assessing natural image quality on SEM datasets,meanwhile,the field of SEM image quality assessment(IQA) is mostly aimed at specific distortion types. In order to solve the above two problems,to address the rich texture, few edges, and extreme sensitivity to the distortion degree of SEM images, we propose a texture and semantic IQA (TSIQA) method for SEM images based on sparse mask and information entropy increase. First, we construct a neural network containing sparse mask module (SMM), which is used to extract intuitive texture features in the spatial and channel domains. Simultaneously, information growth attention (IGA) is introduced into SMM to detect the difference between current and past features of the network for extracting deep semantic information. The quality assessment experiments on SEM image datasets show that compared with the state-of-the-art IQA methods, including popular no-reference techniques adapted to the SEM-IQA, the TSIQA has superiority in typical criteria.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3216388
IEEE ACCESS
Keywords
DocType
Volume
Feature extraction, Distortion, Scanning electron microscopy, Image quality, Data mining, Image quality, Visualization, Semantics, Image texture analysis, Quality assessment, Image quality assessment, no-reference, SEM image, texture representation, semantic information
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Haoran Wang100.34
Li Shiyin22313.56
Jicun Ding300.34
Suyan Li400.34
Liang Dong532652.32
Zhaolin Lu600.68