Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models
- URL: http://arxiv.org/abs/2406.04388v1
- Date: Thu, 6 Jun 2024 15:44:24 GMT
- Title: Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models
- Authors: Gabriel della Maggiora, Luis Alberto Croquevielle, Harry Horsley, Thomas Heinis, Artur Yakimovich,
- Abstract summary: Transport-of-Intensity Equation (TIE) often requires multiple acquisitions at different defocus distances.
We propose to use chromatic aberrations to induce the required through-focus images with a single exposure.
Our contributions offer an alternative TIE approach that leverages chromatic aberrations, achieving accurate single-exposure phase measurement with white light.
- Score: 2.0760654993698426
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology [45.4057289850892]
We propose a speckle-conditioned diffusion model (SpecDiffusion) to reconstruct phase images directly from speckles captured at the detection side of a multi-core fiber (MCF)
Unlike conventional neural networks, SpecDiffusion employs iterative phase denoising steps for speckle-driven phase reconstruction.
SpecDiffusion provides high-fidelity phase reconstruction results and shows powerful generalization capacity for unseen objects.
arXiv Detail & Related papers (2024-07-26T01:42:31Z) - Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing [49.800746112114375]
We propose a novel post-training quantization method (Progressive and Relaxing) for text-to-image diffusion models.
We are the first to achieve quantization for Stable Diffusion XL while maintaining the performance.
arXiv Detail & Related papers (2023-11-10T09:10:09Z) - Learned, Uncertainty-driven Adaptive Acquisition for Photon-Efficient
Multiphoton Microscopy [12.888922568191422]
We propose a method to simultaneously denoise and predict pixel-wise uncertainty for multiphoton imaging measurements.
We demonstrate our method on experimental noisy MPM measurements of human endometrium tissues.
We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data.
arXiv Detail & Related papers (2023-10-24T18:06:03Z) - Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer
for Exposure Correction [65.5397271106534]
A single neural network is difficult to handle all exposure problems.
In particular, convolutions hinder the ability to restore faithful color or details on extremely over-/under- exposed regions.
We propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction.
arXiv Detail & Related papers (2023-09-02T09:07:36Z) - Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle
Phenotypes [0.5076419064097732]
We present an improved CycleGAN architecture that employs self-supervised discriminators to alleviate the need for numerous images.
We also provide results obtained with small biological datasets on obvious and non-obvious cell phenotype variations.
arXiv Detail & Related papers (2023-01-21T16:25:04Z) - Stain-invariant self supervised learning for histopathology image
analysis [74.98663573628743]
We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer.
Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
arXiv Detail & Related papers (2022-11-14T18:16:36Z) - Blind stain separation using model-aware generative learning and its
applications on fluorescence microscopy images [1.713291434132985]
Prior model-based stain separation methods rely on stains' spatial distributions over an image.
Deep generative models are used for this purpose.
In this study, a novel learning-based blind source separation framework is proposed.
arXiv Detail & Related papers (2021-02-12T22:39:39Z) - Towards an Automatic Analysis of CHO-K1 Suspension Growth in
Microfluidic Single-cell Cultivation [63.94623495501023]
We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data.
Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
arXiv Detail & Related papers (2020-10-20T08:36:51Z) - Modality Attention and Sampling Enables Deep Learning with Heterogeneous
Marker Combinations in Fluorescence Microscopy [5.334932400937323]
Fluorescence microscopy allows for a detailed inspection of cells, cellular networks, and anatomical landmarks by staining with a variety of carefully-selected markers visualized as color channels.
Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited.
We propose Marker Sampling and Excite, a neural network approach with a modality sampling strategy and a novel attention module.
arXiv Detail & Related papers (2020-08-27T21:57:07Z) - Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output.
We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
arXiv Detail & Related papers (2020-03-15T23:07:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.