3 research outputs found
Single-shot experimental-numerical twin-image removal in lensless digital holographic microscopy
Lensless digital holographic microscopy (LDHM) offers very large
field-of-view label-free imaging crucial, e.g., in high-throughput particle
tracking and biomedical examination of cells and tissues. Compact layouts
promote point-of-case and out-of-laboratory applications. The LDHM, based on
the Gabor in-line holographic principle, is inherently spoiled by the
twin-image effect, which complicates the quantitative analysis of reconstructed
phase and amplitude maps. Popular family of solutions consists of numerical
methods, which tend to minimize twin-image upon iterative process based on data
redundancy. Additional hologram recordings are needed, and final results
heavily depend on the algorithmic parameters, however. In this contribution we
present a novel single-shot experimental-numerical twin-image removal technique
for LDHM. It leverages two-source off-axis hologram recording deploying simple
fiber splitter. Additionally, we introduce a novel phase retrieval numerical
algorithm specifically tailored to the acquired holograms, that provides
twin-image-free reconstruction without compromising the resolution. We
quantitatively and qualitatively verify proposed method employing phase test
target and cheek cells biosample. The results demonstrate that the proposed
technique enables low-cost, out-of-laboratory LDHM imaging with enhanced
precision, achieved through the elimination of twin-image errors. This
advancement opens new avenues for more accurate technical and biomedical
imaging applications using LDHM, particularly in scenarios where cost-effective
and portable imaging solutions are desired
DeepOrientation: convolutional neural network for fringe pattern orientation map estimation
Fringe pattern based measurement techniques are the state-of-the-art in
full-field optical metrology. They are crucial both in macroscale, e.g., fringe
projection profilometry, and microscale, e.g., label-free quantitative phase
microscopy. Accurate estimation of the local fringe orientation map can
significantly facilitate the measurement process on various ways, e.g., fringe
filtering (denoising), fringe pattern boundary padding, fringe skeletoning
(contouring/following/tracking), local fringe spatial frequency (fringe period)
estimation and fringe pattern phase demodulation. Considering all of that the
accurate, robust and preferably automatic estimation of local fringe
orientation map is of high importance. In this paper we propose novel numerical
solution for local fringe orientation map estimation based on convolutional
neural network and deep learning called DeepOrientation. Numerical simulations
and experimental results corroborate the effectiveness of the proposed
DeepOrientation comparing it with the representative of the classical approach
to orientation estimation called combined plane fitting/gradient method. The
example proving the effectiveness of DeepOrientation in fringe pattern
analysis, which we present in this paper is the application of DeepOrientation
for guiding the phase demodulation process in Hilbert spiral transform. In
particular, living HeLa cells quantitative phase imaging outcomes verify the
method as an important asset in label-free microscopy
Accurate automatic object 4D tracking in digital inâline holographic microscopy based on computationally rendered dark fields
Building on Gabor seminal principle, digital in-line holographic microscopy provides efficient means for space-time investigations of large volumes of interest. Thus, it has a pivotal impact on particle tracking that is crucial in advancing various branches of science and technology, e.g., microfluidics and biophysical processes examination (cell motility, migration, interplay etc.). Well-established algorithms often rely on heavily regularized inverse problem modelling and encounter limitations in terms of tracking accuracy, hologram signal-to-noise ratio, accessible object volume, particle concentration and computational burden. This work demonstrates the DarkTrack algorithm a new approach to versatile, fast, precise, and robust 4D holographic tracking based on deterministic computationally rendered high-contrast dark fields. Its unique capabilities are quantitatively corroborated employing a novel numerical engine for simulating Gabor holographic recording of time-variant volumes filled with predefined dynamic particles. Our solution accounts for multiple scattering and thus it is poised to secure an important gap in holographic particle tracking technology and allow for ground-truth-driven benchmarking and quantitative assessment of tracking algorithms. Proof-of-concept experimental evaluation of DarkTrack is presented via analyzing live spermatozoa. Software supporting both novel numerical holographic engine and DarkTrack algorithm is made open access, which opens new possibilities and sets the stage for democratization of robust holographic 4D particle examination