Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
Not on the map. Why?
Abstract (arXiv)
Quantitative phase imaging (QPI) is a label-free computational imaging technique used in various fields, including biology and medical research. Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction. Here, we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane, all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown, random phase diffusers. This QPI diffractive network is composed of successive diffractive layers, axially spanning in total ~70 wavelengths; unlike existing digital image reconstruction and phase retrieval methods, it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation. This all-optical diffractive processor can provide a low-power, high frame rate and compact alternative for quantitative imaging of phase objects through random, unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing. The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope, performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jan 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 4; 2024: 3; 2025: 2; 2026: 1.
10 papers.
- 2023On the use of deep learning for phase recoverycited by 20
- 2023All-Optical Phase Conjugation Using Diffractive Wavefront Processingcited by 8
- 2023All-optical image denoising using a diffractive visual processorcited by 6
- 2023Pyramid diffractive optical networks for unidirectional image magnification and demagnificationcited by 3
- 2024All-optical complex field imaging using diffractive processorscited by 2
- 2024Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processorcited by 2
- 2024Unidirectional imaging with partially coherent lightcited by 1
- 2025Super-Resolution for Interferometric Imaging: Model Comparisons and Performance Analysiscited by 0
- 2025Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering mediacited by 0
- 2026Wavelength-multiplexed massively parallel diffractive optical information storage and image projectioncited by 0
- 2026Wavelength-multiplexed massively parallel diffractive optical information storage and image projection3 Apr 2026
- 2025Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media28 Nov 2025
- 2025Super-Resolution for Interferometric Imaging: Model Comparisons and Performance Analysis21 Feb 2025
- 2024Unidirectional imaging with partially coherent light10 Aug 2024
- 2024Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor16 Mar 2024
- 2024All-optical complex field imaging using diffractive processors30 Jan 2024
- 2023All-Optical Phase Conjugation Using Diffractive Wavefront Processing8 Nov 2023
- 2023All-optical image denoising using a diffractive visual processor17 Sep 2023
- 2023Pyramid diffractive optical networks for unidirectional image magnification and demagnification29 Aug 2023
- 2023On the use of deep learning for phase recovery2 Aug 2023