Implicit neural image field for biological microscopy image compression

The rapid pace of innovation in biological microscopy has produced increasingly large images, putting pressure on data storage and impeding efficient data sharing, management and visualization. This trend necessitates new, efficient compression solutions, as traditional coder-decoder methods often struggle with the diversity of bioimages, leading to suboptimal results. Here we show an adaptive compression workflow based on implicit neural representation that addresses these challenges. Our approach enables application-specific compression, supports images of varying dimensionality and allows arbitrary pixel-wise decompression. On a wide range of real-world microscopy images, we demonstrate that our workflow achieves high, controllable compression ratios while preserving the critical details necessary for downstream scientific analysis.

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Publication Details

Journal
Nature Computational Science
Published
2025-10-10
DOI
https://doi.org/10.1038/s43588-025-00889-4
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Implicit neural image field for biological microscopy image compression

Shanghang Zhang, Matthias Gunzer, Ali Ata Tuz, Siyuan Qian et al.
Nature Computational Science
Cell Image Analysis Techniques
article

Implicit neural image field for biological microscopy image compression

Shanghang Zhang, Matthias Gunzer, Ali Ata Tuz, Siyuan Qian, Jianxu Chen, Tiejun Huang, Rongyu Zhang, Gaole Dai, Qingpo Wuwu, Yu Zhou, Ming Lu, Shaokang Wang, Cheng-Ching Tseng
article en

Abstract

The rapid pace of innovation in biological microscopy has produced increasingly large images, putting pressure on data storage and impeding efficient data sharing, management and visualization. This trend necessitates new, efficient compression solutions, as traditional coder-decoder methods often struggle with the diversity of bioimages, leading to suboptimal results. Here we show an adaptive compression workflow based on implicit neural representation that addresses these challenges. Our approach enables application-specific compression, supports images of varying dimensionality and allows arbitrary pixel-wise decompression. On a wide range of real-world microscopy images, we demonstrate that our workflow achieves high, controllable compression ratios while preserving the critical details necessary for downstream scientific analysis.

Nature Computational ScienceVol. 5(11)
Peking University (CN), Leibniz Institute for Analytical Sciences - ISAS (DE), University of Duisburg-Essen (DE)
Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China, Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen, National Science and Technology Major Project, Leibniz-Gemeinschaft
Openalex Percentile: Top 80%
Cell Image Analysis Techniques
DOIOpen Access PDFOpenAlex
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Implicit neural image field for biological microscopy image compression — Shanghang Zhang, Matthias Gunzer, et al. · Nature Computational Science (2025) | TGRS Research Map | TGRS