mlsanityData to 7 Oct 2026

Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models

arXiv 2406.04388 ↗First posted 6 Jun 2024eess.IV cs.AI physics.opticscited by 1

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Phase imaging is gaining importance due to its applications in fields like biomedical imaging and material characterization. In biomedical applications, it can provide quantitative information missing in label-free microscopy modalities. One of the most prominent methods in phase quantification is the Transport-of-Intensity Equation (TIE). TIE often requires multiple acquisitions at different defocus distances, which is not always feasible in a clinical setting. To address this issue, we propose to use chromatic aberrations to induce the required through-focus images with a single exposure, effectively generating a through-focus stack. Since the defocus distance induced by the aberrations is small, conventional TIE solvers are insufficient to address the resulting artifacts. We propose Zero-Mean Diffusion, a modified version of diffusion models designed for quantitative image prediction, and train it with synthetic data to ensure robust phase retrieval. Our contributions offer an alternative TIE approach that leverages chromatic aberrations, achieving accurate single-exposure phase measurement with white light and thus improving the efficiency of phase imaging. Moreover, we present a new class of diffusion models that are well-suited for quantitative data and have a sound theoretical basis. To validate our approach, we employ a widespread brightfield microscope equipped with a commercially available color camera. We apply our model to clinical microscopy of patients' urine, obtaining accurate phase measurements.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 23 of its 53 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

23 of 53 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey18 Aug
  2. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  3. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models30 Apr

Other references · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  6. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  7. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  8. 2021Variational Diffusion Modelscited by 1,145
  9. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  10. 2021Score-based diffusion models for accelerated MRIcited by 308
  11. 2022Diffusion Probabilistic Modeling for Video Generationcited by 237
  12. 2023Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localizationcited by 230
8 more
  1. 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 46
  2. 2018PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learningcited by 41
  3. 2022How Much is Enough? A Study on Diffusion Times in Score-based Generative Modelscited by 35
  4. 2022Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonancecited by 21
  5. 2023On the use of deep learning for phase recoverycited by 20
  6. 2020PhaseGAN: A deep-learning phase-retrieval approach for unpaired datasetscited by 15
  7. 2019Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightcited by 5
  8. 2023Conditional Variational Diffusion Modelscited by 5

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Citing papers per year: 2024: 1.

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  1. 2024Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology
  2. 2026Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopy
  3. 2024MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections
  4. 2025Diffusion Algorithm for Metalens Optical Aberration Correction
  5. 2025I2I-PR: Deep Iterative Refinement for Phase Retrieval using Image-to-Image Diffusion Models
  6. 2024Denoising diffusion models for high-resolution microscopy image restoration
  7. 2024From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task
  8. 2026Score-based diffusion models for severely ill-posed problems in diffuse optical tomography