MIDNet:Deblurring Network for Material Microstructure Images  

作　　者：Jiaxiang Wang Zhengyi Li Peng Shi Hongying Yu Dongbai Sun 

机构地区：[1]National Center for Materials Service Safety,University of Science and Technology Beijing,Beijing,100083,China [2]School of Materials,Sun Yat-Sen University,Shenzhen,518107,China [3]School of Materials Science and Engineering,Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Sun Yat-Sen University,Guangzhou,510006,China

出　　处：《Computers, Materials & Continua》2024年第4期1187-1204,共18页计算机、材料和连续体（英文）

基　　金：the National Key R&D Program of China(GrantNo.2021YFA1601104);National KeyR&DProgram of China(GrantNo.2022YFA16038004);National Key R&D Program of China(Grant No.2022YFA16038002);National Science and Technology Major Project of China(No.J2019-VI-0004-0117).

摘　　要：Scanning electron microscopy(SEM)is a crucial tool in the field of materials science,providing valuable insightsinto the microstructural characteristics of materials.Unfortunately,SEM images often suffer from blurrinesscaused by improper hardware calibration or imaging automation errors,which present challenges in analyzingand interpretingmaterial characteristics.Consequently,rectifying the blurring of these images assumes paramountsignificance to enable subsequent analysis.To address this issue,we introduce a Material Images DeblurringNetwork(MIDNet)built upon the foundation of the Nonlinear Activation Free Network(NAFNet).MIDNetis meticulously tailored to address the blurring in images capturing the microstructure of materials.The keycontributions include enhancing the NAFNet architecture for better feature extraction and representation,integratinga novel soft attention mechanism to uncover important correlations between encoder and decoder,andintroducing newmulti-loss functions to improve training effectiveness and overallmodel performance.We conducta comprehensive set of experiments utilizing the material blurry dataset and compare them to several state-of-theartdeblurring methods.The experimental results demonstrate the applicability and effectiveness of MIDNet in thedomain of deblurring material microstructure images,with a PSNR(Peak Signal-to-Noise Ratio)reaching 35.26 dBand an SSIM(Structural Similarity)of 0.946.Our dataset is available at:https://github.com/woshigui/MIDNet.

关 键 词：Image deblurring material microstructure attention mechanism deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]