Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy

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 mic...

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Vydáno v:	Discover nano Ročník 20; číslo 1; s. 102
Hlavní autoři:	Igarashi, Yasuhiko, Nagamura, Naoka, Sekine, Masahiro, Fukidome, Hirokazu, Hino, Hideitsu, Okada, Masato
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 03.07.2025 Springer Nature B.V Springer
Témata:	Algorithms Chemical analysis Chemistry and Materials Science Coding data collection Datasets Deep learning Degradation Dictionaries Electron microscopy Exposure exposure duration High resolution High-resolution microscopy Image quality Image reconstruction Image resolution Materials Science Measurement image analysis Measurement techniques Methods Microscopes Microscopy Misalignment Molecular Medicine Nanochemistry nanomaterials Nanoscale Science and Technology Nanostructure Nanostructure image enhancement Nanotechnology Nanotechnology and Microengineering Neural networks Radiation Radiation damage Radiation damage reduction Radiation effects Sparse coding superresolution Spatial discrimination learning Spatial resolution Spectroscopy Spectrum analysis Synchrotron image reconstruction Synchrotron radiation Time measurement X-rays Nanostructure image enhancement Radiation damage reduction Sparse coding superresolution Synchrotron image reconstruction High-resolution microscopy Measurement image analysis
ISSN:	2731-9229, 1931-7573, 2731-9229, 1556-276X
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Shrnutí:	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 , 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2731-9229 1931-7573 2731-9229 1556-276X
DOI:	10.1186/s11671-025-04291-x