# Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy

https://mdr.nims.go.jp/datasets/144e363b-1e09-45ec-b868-7605b78f0e2d

## File

- [DiscoverNano(2025).pdf](https://mdr.nims.go.jp/filesets/8f3835f6-414a-4f0e-9536-f8331f0b5697/download) ([Detail](https://mdr.nims.go.jp/filesets/8f3835f6-414a-4f0e-9536-f8331f0b5697.md))

## Id

144e363b-1e09-45ec-b868-7605b78f0e2d

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-10-29T09:30:07.203663Z

## Updated at

2026-11-01T23:48:23.710412Z

## Published at

2026-11-02T01:32:54.353997Z

## Doi



## First published url

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

## Date published

2025-07-03

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Sparse coding-based multiframe superresolution for efficient synchrotron
    radiation microspectroscopy
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Yasuhiko Igarashi
  role: author
- name: Naoka Nagamura
  role: author
  orcid: https://orcid.org/0000-0002-7697-8983
  organization: National Institute for Materials Science
- name: Masahiro Sekine
  role: author
- name: Hirokazu Fukidome
  role: author
- name: Hideitsu Hino
  role: author
- name: Masato Okada
  role: author

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Sparse coding superresolution
  schema: not_defined
- subject: Nanostructure image enhancement
  schema: not_defined
- subject: Measurement image analysis
  schema: not_defined
- subject: Synchrotron image reconstruction
  schema: not_defined
- subject: High-resolution microscopy
  schema: not_defined
- subject: Radiation damage reduction
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/
  date_licensed: 2025-07-03

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Discover Nano
  issn: '27319229'
  volume: '20'
  issue: '1'
  article_number: '102'

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## Funding

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

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## Fileset

- id: 8f3835f6-414a-4f0e-9536-f8331f0b5697
  filename: DiscoverNano(2025).pdf
  content_type: application/pdf
  size: 4216484
  md5: 1b9b5b62ca01628826b13944f2017e39

## Thumbnail

fileset_id: 8f3835f6-414a-4f0e-9536-f8331f0b5697
filename: DiscoverNano(2025).pdf