@inproceedings{165b96b0b3dc4812a1a9cc61408a2651,
title = "Complex-Valued Retrievals From Noisy Images Using Diffusion Models",
abstract = "In diverse microscopy modalities, sensors measure only real-valued intensities. Additionally, the sensor readouts are affected by Poissonian-distributed photon noise. Traditional restoration algorithms typically aim to minimize the mean squared error (MSE) between the original and recovered images. This often leads to blurry outcomes with poor perceptual quality. Recently, deep diffusion models (DDMs) have proven to be highly capable of sampling images from the a-posteriori probability of the sought variables, resulting in visually pleasing high-quality images. These models have mostly been suggested for real-valued images suffering from Gaussian noise. In this study, we generalize annealed Langevin Dynamics, a type of DDM, to tackle the fundamental challenges in optical imaging of complex-valued objects (and real images) affected by Poisson noise. We apply our algorithm to various optical scenarios, such as Fourier Ptychography, Phase Retrieval, and Poisson denoising. Our algorithm is evaluated on simulations and biological empirical data.",
keywords = "Diffusion Models, Langevin Dynamics, Microscopy, Optical Imaging, Phase Retrieval, Poisson Denoising, Ptychogarphy",
author = "Nadav Torem and Roi Ronen and Schechner, {Yoav Y.} and Michael Elad",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 ; Conference date: 02-10-2023 Through 06-10-2023",
year = "2023",
doi = "https://doi.org/10.1109/ICCVW60793.2023.00412",
language = "الإنجليزيّة",
series = "Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023",
pages = "3812--3822",
booktitle = "Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023",
}