HoloADMM: High-Quality Holographic Complex Field Recovery
Abstract
Holography enables intriguing microscopic imaging modalities, particularly through Quantitative Phase Imaging (QPI), which utilizes the phase of coherent light as a way to reveal the contrast in transparent and thin microscopic specimens. Despite the limitation of image sensors, which detect only light intensity, phase information can still be recorded within a two-dimensional interference pattern between two distinct light waves. Numerical reconstruction is later needed to retrieve the amplitude and phase from such holographic measurements. To this end, we introduce HoloADMM, a novel interpretable, learning-based approach for in-line holographic image reconstruction. HoloADMM enhances imaging capability with spatial image super-resolution, offering a versatile framework that accommodates multiple illumination wavelengths and supports extensive refocusing ranges with up to 10 µm precision. Our results indicate a substantial improvement in reconstruction quality over existing methods and demonstrate HoloADMM’s effective adaptation to real holographic data captured by our Digital in-line Holographic Microscope (DIHM). This work not only advances holographic imaging techniques but also broadens the potential for non-invasive microscopic analysis applications.
Cite
Text
Mel et al. "HoloADMM: High-Quality Holographic Complex Field Recovery." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73209-6_8Markdown
[Mel et al. "HoloADMM: High-Quality Holographic Complex Field Recovery." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/mel2024eccv-holoadmm/) doi:10.1007/978-3-031-73209-6_8BibTeX
@inproceedings{mel2024eccv-holoadmm,
title = {{HoloADMM: High-Quality Holographic Complex Field Recovery}},
author = {Mel, Mazen and Springer, Paul and Zanuttigh, Pietro and Zhou, Haitao and Gatto, Alexander},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-73209-6_8},
url = {https://mlanthology.org/eccv/2024/mel2024eccv-holoadmm/}
}