Physics-driven universal twin-image removal network for digital in-line holographic microscopy
arXiv:2308.04471 · doi:10.1364/OE.505440
Abstract
Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, and bio-microfluidics. However, the quality of DIHM reconstructions is compromised by twin-image noise, posing a significant challenge. Conventional methods for mitigating this noise involve complex hardware setups or time-consuming algorithms with often limited effectiveness. In this work, we propose UTIRnet, a deep learning solution for fast, robust, and universally applicable twin-image suppression, trained exclusively on numerically generated datasets. The availability of open-source UTIRnet codes facilitates its implementation in various DIHM systems without the need for extensive experimental training data. Notably, our network ensures the consistency of reconstruction results with input holograms, imparting a physics-based foundation and enhancing reliability compared to conventional deep learning approaches. Experimental verification was conducted among others on live neural glial cell culture migration sensing, which is crucial for neurodegenerative disease research.
References in corpus (5)
- Wide-field, high-resolution Fourier ptychographic microscopy
- Phase recovery and holographic image reconstruction using deep learning in neural networks
- Physics-driven universal twin-image removal network for digital in-line holographic microscopy
- Single-shot experimental-numerical twin-image removal in lensless digital holographic microscopy
- DeepOrientation: convolutional neural network for fringe pattern orientation map estimation