We present a new method to achieve digital autofocus in holography. This method relies on the insertion of calibrated beads into the studied sample. Reconstructing the position and the radius of the beads using Inverse Problems Approach, based on Mie Model, makes it possible to accurately locate the slide on which the sampled is placed. Numerical focusing can then be performed using the standard backpropagation method or regularized reconstruction. Because the reconstruction plane can be chosen objectively with respect to the position of the slide, this numerical autofocus is reproducible whatever is the type of the observed biological sample.
The in-line configuration of digital holographic microscopy is the simplest to set up, but it requires numerical reconstructions, in order to retrieve the phase of the wave diffracted by the sample. These reconstructions are based on an image formation model, but the effect of the microscopy system is often neglected. Yet, some parameters, like a wrong magnification, the partial coherence of the illumination, or optical aberrations may lead to bias in the reconstruction. In the framework of inverse problems approaches, we analysed and studied the effects of some of these parameters using simulations and experiments on calibrated spherical objects and a rigorous model (Lorenz-Mie), in order to evaluate the relevance and requirements of model refinements.
Unstained biological samples (e.g. cells or bacteria) are mostly transparent objects, optically described by their optical thickness and refractive index changes. The knowledge of this information could help to better identify or at least classify cells according to their types or state. Holographic microscopy techniques are effective methods to obtain quantitative phase profiles of biological samples. These techniques, however, may require high temporal stability to measure cell thickness fluctuations. A simple and low-cost way to ensure temporal stability consists in using a “common path” configuration. In this configuration the reference and signal beams follow the same optical path, leading to high temporal stability. The beam paths are split by a glass plate whose thickness introduces a lateral shift between the beams, reflected by the front and back surfaces. This configuration is an off-axis holographic microscopy setup since the glass plate introduces an angle between the two reflected spherical wavefronts. The inverse problem approach proposes to reconstruct the objects directly from the holograms without any filtering of the signal and with prior information on the objects. In this framework, a good knowledge of the image formation model is important. We propose a reconstruction algorithm based on a parametric inverse problem approach to reconstruct phase objects holograms acquired by the lateral shearing digital holographic system. Assuming the noise in the data to be white and Gaussian, it mainly consists in fitting a model to the data. The algorithm is applied to silica micro-beads on out-of-focus off-axis holograms recorded with the lateral shearing configuration.
Digital holographic microscopy can image both absorbing and translucent objects. Due to the presence of twin-images and out-of-focus objects, the task of segmenting the objects from a back-propagated hologram is challenging. This paper investigates the use of deep neural networks to combine the real and imaginary parts of the back-propagated wave and produce a segmentation. The network, trained with pairs of back-propagated simulated holograms and ground truth segmentations, is shown to perform well even in the case of a mismatch between the defocus distance of the holograms used during the training step and the actual defocus distance of the holograms at test time.
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