17 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
Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging
Copyright © 2019 American Chemical Society. In stimulated emission depletion (STED) nanoscopy, the major origin of decreased signal-to-noise ratio within images can be attributed to sample photobleaching and strong optical aberrations. This is due to STED utilizing a high-power depletion laser (increasing the risk of photodamage), while the depletion beam is very sensitive to sample-induced aberrations. Here, we demonstrate a custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue. We introduce and investigate a state of the art image denoising method by block-matching and collaborative 3D filtering (BM3D) to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality. Numerical denoising provides an increase in the final effective resolution of the STED imaging of 31% using the well established Fourier ring correlation metric. Results achieved through the combination of aberration correction and tailored image processing are experimentally validated through super-resolved 3D imaging of axons in differentiated induced pluripotent stem cells growing under an 80 μm thick layer of tissue with lateral and axial resolution of 204 and 310 nm, respectively
Temporal phase unwrapping using deep learning
The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical
phase unwrapping algorithm for fringe projection profilometry (FPP), is capable
of eliminating the phase ambiguities even in the presence of surface
discontinuities or spatially isolated objects. For the simplest and most
efficient case, two sets of 3-step phase-shifting fringe patterns are used: the
high-frequency one is for 3D measurement and the unit-frequency one is for
unwrapping the phase obtained from the high-frequency pattern set. The final
measurement precision or sensitivity is determined by the number of fringes
used within the high-frequency pattern, under the precondition that the phase
can be successfully unwrapped without triggering the fringe order error.
Consequently, in order to guarantee a reasonable unwrapping success rate, the
fringe number (or period number) of the high-frequency fringe patterns is
generally restricted to about 16, resulting in limited measurement accuracy. On
the other hand, using additional intermediate sets of fringe patterns can
unwrap the phase with higher frequency, but at the expense of a prolonged
pattern sequence. Inspired by recent successes of deep learning techniques for
computer vision and computational imaging, in this work, we report that the
deep neural networks can learn to perform TPU after appropriate training, as
called deep-learning based temporal phase unwrapping (DL-TPU), which can
substantially improve the unwrapping reliability compared with MF-TPU even in
the presence of different types of error sources, e.g., intensity noise, low
fringe modulation, and projector nonlinearity. We further experimentally
demonstrate for the first time, to our knowledge, that the high-frequency phase
obtained from 64-period 3-step phase-shifting fringe patterns can be directly
and reliably unwrapped from one unit-frequency phase using DL-TPU
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
Single-shot fringe pattern phase retrieval using improved period-guided bidimensional empirical mode decomposition and Hilbert transform
Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD – we have considerably increased algorithm’s application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data
Versatile optimization-based speed-up method for autofocusing in digital holographic microscopy
We propose a speed-up method for the in-focus plane detection in digital holographic microscopy that can be applied to a broad class of autofocusing algorithms that involve repetitive propagation of an object wave to various axial locations to decide the in-focus position. The classical autofocusing algorithms apply a uniform search strategy, i.e., they probe multiple, uniformly distributed axial locations, which leads to heavy computational overhead. Our method substantially reduces the computational load, without sacrificing the accuracy, by skillfully selecting the next location to investigate, which results in a decreased total number of probed propagation distances. This is achieved by applying the golden selection search with parabolic interpolation, which is the gold standard for tackling single-variable optimization problems. The proposed approach is successfully applied to three diverse autofocusing cases, providing up to 136-fold speed-up
Hilbert phase microscopy based on pseudo thermal illumination in Linnik configuration
Quantitative phase microscopy (QPM) is often based on recording an
object-reference interference pattern and its further phase demodulation. We
propose Pseudo Hilbert Phase Microscopy (PHPM) where we combine pseudo thermal
light source illumination and Hilbert spiral transform phase demodulation to
achieve hybrid hardware-software-driven noise robustness and increase in
resolution of single-shot coherent QPM. Those advantageous features stem from
physically altering the laser spatial coherence and numerically restoring
spectrally overlapped object spatial frequencies. Capabilities of the PHPM are
demonstrated analyzing calibrated phase targets and live HeLa cells in
comparison with laser illumination and phase demodulation via temporal phase
shifting and Fourier transform techniques. Performed studies verified unique
ability of the PHPM to couple single-shot imaging, noise minimization, and
preservation of phase details
Accurate automatic object 4D tracking in digital in‑line 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
Optically-sectioned two-shot structured illumination microscopy with Hilbert-Huang processing
We introduce a fast, simple, adaptive and experimentally robust method for reconstructing background-rejected optically-sectioned images using two-shot structured illumination microscopy. Our innovative data demodulation method needs two grid-illumination images mutually phase shifted by π (half a grid period) but precise phase displacement between two frames is not required. Upon frames subtraction the input pattern with increased grid modulation is obtained. The first demodulation stage comprises two-dimensional data processing based on the empirical mode decomposition for the object spatial frequency selection (noise reduction and bias term removal). The second stage consists in calculating high contrast image using the two-dimensional spiral Hilbert transform. Our algorithm effectiveness is compared with the results calculated for the same input data using structured-illumination (SIM) and HiLo microscopy methods. The input data were collected for studying highly scattering tissue samples in reflectance mode. Results of our approach compare very favorably with SIM and HiLo techniques