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説明:

In nanostructure extraction, advanced techniques like synchrotron radiation and electron microscopy are often hindered by radiation damage and charging artifacts from long exposure times. This study presents a multiframe superresolution method using sparse coding to enhance synchrotron radiation microspectroscopy images. By reconstructing high-resolution images from multiple low-resolution ones, exposure time is minimized, reducing radiation effects, thermal drift, and sample degradation while preserving spatial resolution. Unlike deep learning based superresolution methods, which overlook positional misalignment, our approach treats positional shifts as known control parameters, enhancing superresolution accuracy with a small, noisy dataset. Additionally, our sparse coding method learns an optimal dictionary tailored for nanostructure extraction, fine tuning the SR process to the unique characteristics of the data, even with noise and limited samples. Applied to 3D nanoscale electron spectroscopy for chemical analysis (nano-ESCA) data, our method, utilizing a high-resolution dictionary learned from 3D nano-ESCA datasets, significantly improves image quality, preserving structural details. Unlike state-of-the-art deep learning techniques that require large datasets, our method excels with limited data, making it ideal for real-world scenarios with constrained sample sizes. High-resolution quality can be maintained while reducing the measurement time by over 40%, highlighting the efficiency of our approach. The results underscore the potential of this superresolution technique to not only advance synchrotron radiation microspectroscopy but also to be adapted for other high-resolution imaging modalities, such as electron microscopy. This approach offers enhanced image quality, reduced exposure times, and improved interpretability of scientific data, making it a versatile tool for overcoming the challenges associated with radiation damage and sample degradation in nanoscale imaging.

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

キーワード: Sparse coding superresolution, Nanostructure image enhancement, Measurement image analysis, Synchrotron image reconstruction, High-resolution microscopy, Radiation damage reduction

刊行年月日: 2025-07-03

出版者: Springer Science and Business Media LLC

掲載誌:

• Discover Nano (ISSN: 27319229) vol. 20 issue. 1 102

研究助成金:

• Japan Society for the Promotion of Science 24K02995
• Japan Society for the Promotion of Science 21H04696
• Japan Society for the Promotion of Science 24K01243
• Japan Science and Technology Agency CREST: JPMJCR1761
• Japan Science and Technology Agency JPMJCR21O1
• Japan Science and Technology Agency JPMJCR2235
• Japan Science and Technology Agency PRESTO: 590 JPMJPR17N2,
• Japan Science and Technology Agency JPMJPR17N2
• Japan Science and Technology Agency JPMJPR20T7
• Innovative Science and Technology Initiative for Security of ATLA Japan JPJ004596
• Innovative Science and Technology Initiative for Security of ATLA Japan JPJ004596
• National Institute of Information and Communications Technology Beyond 5G Research and Development Project(05901)

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1186/s11671-025-04291-x

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更新時刻: 2026-11-02 08:48:23 +0900

MDRでの公開時刻: 2026-11-02 10:32:54 +0900