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Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy

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Bibliographic Details
Title: Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy
Authors: Yasuhiko Igarashi, Naoka Nagamura, Masahiro Sekine, Hirokazu Fukidome, Hideitsu Hino, Masato Okada
Source: Discover Nano, Vol 20, Iss 1, Pp 1-20 (2025)
Publisher Information: Springer, 2025.
Publication Year: 2025
Collection: LCC:Materials of engineering and construction. Mechanics of materials
Subject Terms: Sparse coding superresolution, Nanostructure image enhancement, Measurement image analysis, Synchrotron image reconstruction, High-resolution microscopy, Radiation damage reduction, Materials of engineering and construction. Mechanics of materials, TA401-492
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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2731-9229
Relation: https://doaj.org/toc/2731-9229
DOI: 10.1186/s11671-025-04291-x
Access URL: https://doaj.org/article/c0ef5eb6ee134bd79a11c03e2c2972f1
Accession Number: edsdoj.0ef5eb6ee134bd79a11c03e2c2972f1
Database: Directory of Open Access Journals
Description
Abstract: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.
ISSN:27319229
DOI:10.1186/s11671-025-04291-x