Calibration-free quantitative phase imaging using data-driven aberration modeling
arXiv:2007.13038 · doi:10.1364/OE.412009
Abstract
We present a data-driven approach to compensate for optical aberration in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells, benchmarking against the conventional method using background subtractions.
References in corpus (10)
- Wide-field, high-resolution Fourier ptychographic microscopy
- Phase recovery and holographic image reconstruction using deep learning in neural networks
- Active illumination using a digital micromirror device for quantitative phase imaging
- Low Photon Count Phase Retrieval Using Deep Learning
- Time-multiplexed structured illumination using a DMD for optical diffraction tomography
- Cross-modality deep learning brings bright-field microscopy contrast to holography
- Deep learning approach to coherent noise reduction in optical diffraction tomography
- White-light Quantitative Phase Imaging Unit
- The effects of spatiotemporal coherence on interferometric imaging
- Compensation of aberration and speckle noise in quantitative phase imaging using lateral shifting and spiral phase integration