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      Deep Convolutional Neural Network Based Image Denoising in STEM

      Published
      conference-abstract
      1 , , 2 , 1 , 3 , 4 , 5 , 2 , 3
      13th Asia Pacific Microscopy Congress 2025 (APMC13)
      2-7 Febuary 2025
      STEM, Machine Learning, Deep Neural Nets
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            Abstract

            Content

            Author and article information

            Conference
            21 January 2025
            : e250
            Affiliations
            [1 ]Distributed Algorithms Centre of Doctoral Training, University of Liverpool, Liverpool UK
            [2 ]SenseAI Innovations Ltd., Brodie Tower, University of Liverpool, Liverpool, UK
            [3 ]Mechanical Materials and Aerospace Engineering, University of Liverpool, Liverpool UK
            [4 ]Department of Computer Science, University of Liverpool, Liverpool, UK
            [5 ]Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
            Author notes
            Author information
            https://orcid.org/0009-0000-6382-2464
            Article
            10.14293/APMC13-2025-0250
            3e97d00c-f94c-4d69-9348-d2c174d40549
            2025 The Authors.

            Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

            13th Asia Pacific Microscopy Congress 2025
            APMC13
            13
            Brisbane, Australia
            2-7 Febuary 2025
            History
            Categories
            ID01 - Image Analysis, Data Handling, Big Data & AI

            Deep Neural Nets,STEM,Machine Learning

            References

            1. D. Nicholls et al, Nanoscale, 12.41 (2020), p. 21248–21254. 10.1039/D0NR04589F.

            2. D. Nicholls et al, Ultramicroscopy, 233 (2022), 10.1016/j.ultramic.2021.113451.

            3. V. Jain et al, Advances in Neural Information Processing Systems, 21 (2008).

            4. Z. Li, et al, IEEE Trans. Neural Netw. Learn. Syst, 33 (2022), 10.1109/TNNLS.2021.3084827.

            5. L. Gambini et al, Mach. Learn.: Sci. Technol, 4 (2023), 10.1088/2632-2153/acbb52.

            6. Q. Tian, et al, NeuroImage, 253 (2022), 10.1016/j.neuroimage.2022.119033.

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