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README.md

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# Bayesian MEF
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[![PyPI](https://img.shields.io/pypi/v/bayes_mef)](https://pypi.org/project/bayes_mef/)
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![Python 3.9+](https://img.shields.io/badge/python-3.9+-green.svg)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10809893.svg)](https://doi.org/10.5281/zenodo.10809893)
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[![DOI](https://zenodo.org/badge/769216428.svg)](https://zenodo.org/doi/10.5281/zenodo.11103003)
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[![License](https://img.shields.io/badge/License-BSD_3--Clause-purple.svg)](https://opensource.org/licenses/BSD-3-Clause)
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Bayesian multi-exposure image fusion (MEF) is a general-purpose algorithm to achieve robust high dynamic range (HDR) imaging, particularly in scenarios with low signal-to-noise ratio (SNR) or variations in illumination intensity. This approach is especially crucial for high quality phase retrieval in coherent diffractive imaging (CDI). The algorithm, detailed in the [arXiv preprint](https://arxiv.org/abs/2403.11344), primarily focuses on its implementation and demonstrates the benefits in ptychography. To reproduce the results in the paper, see this [section](#reproducing-results). However, to get started, please install the package and check the demo usage below.

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