ArticleCommunications engineering2024
Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model.
Article in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026Article
- Dual-scale and dual-channel oblique plane microscopy for multi-resolution imaging of large tissue specimens.Biomedical optics express · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.Communications engineering · 2026Article
- Interdisciplinary Nanomaterials for Biomedical Imaging and Sensing Applications.Nanomaterials (Basel, Switzerland) · 2025Review
- Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy.Small methods · 2025Article
- Rapid eigenpatch utility classifier for image denoising.Scientific reports · 2025Article
- Dynamic Mode Decomposition of Multiphoton and Stimulated Emission Depletion Microscopy Data for Analysis of Fluorescent Probes in Cellular Membranes.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Light microscopy is a practical tool for advancing biomedical research and diagnostics, offering invaluable insights into the cellular and subcellular structures of living organisms. However, diffraction and optical imperfections actively hinder the attainment of high-quality images. In recent years, there has been a growing interest in applying deep learning techniques to overcome these challenges in light microscopy imaging. Nonetheless, the resulting reconstructions often suffer from undesirable artefacts and hallucinations. Here, we introduce a deep learning-based approach that incorporates the fundamental physics of light propagation in microscopy into the loss function. This model employs a conditioned diffusion model in a physics-informed architecture. To mitigate the issue of limited available data, we utilise synthetic datasets for training purposes. Our results demonstrate consistent enhancements in image quality and substantial reductions in artefacts when compared to state-of-the-art methods. The presented technique is intuitively accessible and allows obtaining higher quality microscopy images for biomedical studies.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.