Combining Transformer and CNN for Super-Resolution of Animal Fiber Microscopy Images

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dc.contributor.authorLi, Jiagenen_US
dc.contributor.authorJi, Yatuen_US
dc.contributor.authorLu, Minen_US
dc.contributor.authorWang, Lien_US
dc.contributor.authorDai, Lingjieen_US
dc.contributor.authorXu, Xuanxuanen_US
dc.contributor.authorWu, Nieren_US
dc.contributor.authorLiu, Naen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:42:55Z
dc.date.available2023-10-09T07:42:55Z
dc.date.issued2023
dc.description.abstractThe images of cashmere and wool fibers used for scientific research in the textile field are mostly acquired manually under an optical microscope. However, due to the interference of microscope quality, shooting environment, focal length selection, acquisition techniques and other factors, the quality of the obtained photographs tends to have a low resolution, and it is difficult to display the fine fiber texture structure and scale details. To address the above problems, a lightweight super-resolution reconstruction algorithm with multi-scale hierarchical screening is proposed. Specifically, firstly, a hybrid module incorporating SwinTransformer and enhanced channel attention is proposed to extract the global features and obtain the important localization among them, in addition, a multi-scale hierarchical screening filtering module is proposed based on the residual model, which amplifies the feature information focusing on high-frequency regions by splitting the channels to allow the model to adaptively weight the features according to the feature weights and amplifies the feature information focusing on high-frequency regions. Finally, the global average pooling attention module integrates and weights the high-frequency features again to enhance details such as edges and textures. A large number of experiments show that compared with other state-of-the-art algorithms, the proposed method significantly improves the image quality on the fiber dataset, and at the same time proves the effectiveness of the proposed method at all scales in five public datasets, occupies less memory parameters than SwinIR, and not only improves the PSNR and SSIM, but also reduces the parameters compared with the light-weight ESRT.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationPacific Graphics Short Papers and Posters
dc.identifier.doi10.2312/pg.20231283
dc.identifier.isbn978-3-03868-234-9
dc.identifier.pages115-116
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20231283
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20231283
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Computer graphics; Image processing
dc.subjectComputing methodologies
dc.subjectComputer graphics
dc.subjectImage processing
dc.titleCombining Transformer and CNN for Super-Resolution of Animal Fiber Microscopy Imagesen_US
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