Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy
Abstract 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 radi...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Nano |
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| Online Access: | https://doi.org/10.1186/s11671-025-04291-x |
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| author | Yasuhiko Igarashi Naoka Nagamura Masahiro Sekine Hirokazu Fukidome Hideitsu Hino Masato Okada |
| author_facet | Yasuhiko Igarashi Naoka Nagamura Masahiro Sekine Hirokazu Fukidome Hideitsu Hino Masato Okada |
| author_sort | Yasuhiko Igarashi |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c0ef5eb6ee134bd79a11c03e2c2972f1 |
| institution | Kabale University |
| issn | 2731-9229 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Nano |
| spelling | doaj-art-c0ef5eb6ee134bd79a11c03e2c2972f12025-08-20T03:37:40ZengSpringerDiscover Nano2731-92292025-07-0120112010.1186/s11671-025-04291-xSparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopyYasuhiko Igarashi0Naoka Nagamura1Masahiro Sekine2Hirokazu Fukidome3Hideitsu Hino4Masato Okada5Institute of Engineering, Information and Systems, University of TsukubaPhotoemission Group, National Institute for Materials ScienceInstitute of Engineering, Information and Systems, University of TsukubaResearch Institute of Electrical Communication, Tohoku UniversityDepartment of Advanced Data Science, Research Center for Statistical Machine Learning, The Institute of Statistical MathematicsPhotoemission Group, National Institute for Materials ScienceAbstract 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.https://doi.org/10.1186/s11671-025-04291-xSparse coding superresolutionNanostructure image enhancementMeasurement image analysisSynchrotron image reconstructionHigh-resolution microscopyRadiation damage reduction |
| spellingShingle | Yasuhiko Igarashi Naoka Nagamura Masahiro Sekine Hirokazu Fukidome Hideitsu Hino Masato Okada Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy Discover Nano Sparse coding superresolution Nanostructure image enhancement Measurement image analysis Synchrotron image reconstruction High-resolution microscopy Radiation damage reduction |
| title | Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy |
| title_full | Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy |
| title_fullStr | Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy |
| title_full_unstemmed | Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy |
| title_short | Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy |
| title_sort | sparse coding based multiframe superresolution for efficient synchrotron radiation microspectroscopy |
| topic | Sparse coding superresolution Nanostructure image enhancement Measurement image analysis Synchrotron image reconstruction High-resolution microscopy Radiation damage reduction |
| url | https://doi.org/10.1186/s11671-025-04291-x |
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