76 research outputs found
Computational Imaging for Phase Retrieval and Biomedical Applications
In conventional imaging, optimizing hardware is prioritized to enhance image quality directly. Digital signal processing is viewed as supplementary. Computational imaging intentionally distorts images through modulation schemes in illumination or sensing. Then its reconstruction algorithms extract desired object information from raw data afterwards. Co-designing hardware and algorithms reduces demands on hardware and achieves the same or even better image quality. Algorithm design is at the heart of computational imaging, with model-based inverse problem or data-driven deep learning methods as approaches. This thesis presents research work from both perspectives, with a primary focus on the phase retrieval issue in computational microscopy and the application of deep learning techniques to address biomedical imaging challenges.
The first half of the thesis begins with Fourier ptychography, which was employed to overcome chromatic aberration problems in multispectral imaging. Then, we proposed a novel computational coherent imaging modality based on Kramers-Kronig relations, aiming to replace Fourier ptychography as a non-iterative method. While this approach showed promise, it lacks certain essential characteristics of the original Fourier ptychography. To address this limitation, we introduced two additional algorithms to form a whole package scheme. Through comprehensive evaluation, we demonstrated that the combined scheme outperforms Fourier ptychography in achieving high-resolution, large field-of-view, aberration-free coherent imaging.
The second half of the thesis shifts focus to deep-learning-based methods. In one project, we optimized the scanning strategy and image processing pipeline of an epifluorescence microscope to address focus issues. Additionally, we leveraged deep-learning-based object detection models to automate cell analysis tasks. In another project, we predicted the polarity status of mouse embryos from bright field images using adapted deep learning models. These findings highlight the capability of computational imaging to automate labor-intensive processes, and even outperform humans in challenging tasks.</p
Near Field Electron Ptychography
Phase imaging in the Transmission Electron Microscope (TEM) has a long history, from the implementation of off-axis holography in TEM to Differential Phase Contrast (DPC) on the Scanning Transmission Electron Microscopy (STEM). The advent of modern computing has enabled the development of iterative algorithms which attempt to recover a phase image of a specimen from measurements of the way it diffracts an incident electron beam. One of the most successful of these iterative methods is focused probe ptychography, which relies on far field diffraction pattern measurements recorded as the incident beam is scanned through a grid of locations across the specimen. Focused probe ptychography implemented in the STEM has provided the highest resolution images available to date, allows for lens-less setups avoiding the aberrations typical in older STEMs and allows for simultaneous reconstruction of the illumination and specimen. Ptychography is computationally flexible (highly constrained), allowing for additional unknowns other than the phase of the specimen to be recovered, for example positions can be refined during reconstruction.
Near field ptychography is a recent variation on ptychography that replaces the far-field diffraction data with diffraction patterns recorded in the near field, or Fresnel, region. It promises to obtain a much larger field of view with fewer diffraction patterns than focused probe ptychography. The main contribution of this thesis is the implementation of a new form of near field ptychography on the Transmission Electron Microscope (TEM), using an etched silicon nitride window to structure the electron beam. Proof-of-concept results show the method quantitatively recovers megapixel phase images from as few as 9 recorded diffraction patterns, compared to many hundreds of diffraction patterns required for focused probe ptychography. Additional sets of results show how near-field ptychography can recover extremely large fields of view, deal effectively with inelastic scattering, and accommodate several sources of uncertainty in the experimental process.
Further contributions in the thesis include: experiments and results from visible-light versions of near field ptychography, which explain its limitations and practical application; a description and code for analysis tools that are used to assess phase imaging performance; DigitalMicrograph (DM) code and a data collection workflow to realise TEM-based near-field ptychography; details of the design, realisation and performance of the etched silicon nitride windows; and simulation studies aimed at furthering understanding of the frequency response of the technique. Future work is outlined, focusing on potential applications in a wide range of real-world specimens and improved TEM setups to implement near field ptychography
Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy
Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced setups, acquisition modalities or where uncertainty is high; the need for complex computational methods clashes with rapid design and execution. In all these cases, Automatic Differentiation, one of the subtopics of Artificial Intelligence, may offer a functional solution, but only if a GPU implementation is available. In this paper, we show how a framework built to solve just one optimisation problem can be employed for many different X-ray imaging inverse problems
Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques
The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste.
A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions
On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from
its intensity measurements. As exemplified from quantitative phase imaging and
coherent diffraction imaging to adaptive optics, PR is essential for
reconstructing the refractive index distribution or topography of an object and
correcting the aberration of an imaging system. In recent years, deep learning
(DL), often implemented through deep neural networks, has provided
unprecedented support for computational imaging, leading to more efficient
solutions for various PR problems. In this review, we first briefly introduce
conventional methods for PR. Then, we review how DL provides support for PR
from the following three stages, namely, pre-processing, in-processing, and
post-processing. We also review how DL is used in phase image processing.
Finally, we summarize the work in DL for PR and outlook on how to better use DL
to improve the reliability and efficiency in PR. Furthermore, we present a
live-updating resource (https://github.com/kqwang/phase-recovery) for readers
to learn more about PR.Comment: 82 pages, 32 figure
Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics
Optical measurements often exhibit mixed Poisson-Gaussian noise statistics,
which hampers image quality, particularly under low signal-to-noise ratio (SNR)
conditions. Computational imaging falls short in such situations when solely
Poissonian noise statistics are assumed. In response to this challenge, we
define a loss function that explicitly incorporates this mixed noise nature. By
using maximum-likelihood estimation, we devise a practical method to account
for camera readout noise in gradient-based ptychography optimization. Our
results, based on both experimental and numerical data, demonstrate that this
approach outperforms the conventional one, enabling enhanced image
reconstruction quality under challenging noise conditions through a
straightforward methodological adjustment.Comment: Contains main and supplementary document
Synchrotron X-ray operando studies of atomic structure evolution of multi-component Al alloys in liquid state
This research has studied one of the challenging scientific issues in materials science, i.e., in real time, understanding quantitatively the 3D atomic structures of multiple component alloys in the liquid state and how the atomic structures evolve with temperatures until the onset of crystal nucleation. Four Al-based alloys were used in the research: (1) Al-0.4Sc, (2) Al-1.5Fe, (3) Al-5Cu-1.5Fe and (4) Al-5Cu-1.5Fe-1Si alloy (all in weight percentage). All alloys were heated up to the liquid state and then cooled down with predefined cooling rates using a dedicated solidification apparatus. During cooling, synchrotron X-ray was used to illuminate onto the samples and the total scattering data were collected at the target temperatures. Based on the total scattering data, the empirical potential structure refinement (EPSR) method was used to model and reconstruct the 3D atomic structures in the liquid state at the selected temperatures for each alloy. The research has demonstrated that the EPSR is a computationally efficient tool for searching and finding the solutions of 3D atomic structures according to the measured total scattering data. For the studied alloys, the research reveals fully the temperature-dependent structure heterogeneity and their evolutions with temperature. The key findings of the research are: (1) For the Al-0.4Sc alloy, at the short-range scale in the liquid state, Sc-centred Al polyhedrons form icosahedral type structures with the Al coordination number in the range of 10–12. As the melt is cooled down, the Sc-centred polyhedrons become more compacted, and the connections between adjacent polyhedrons change from more vertex connection to more edge and then more face-sharing connection. At the medium-range scale, the Sccentred clusters with face-sharing are proved to be the “precursors” for the L12 Al3Sc primary phase in the liquid-solid coexisting region. (2) For the three Fe-containing alloys, atomic structural heterogeneities were found to exist in the 1st atomic shell and beyond. The degree of structural heterogeneities is related with the difference in atom radius, atomic bond length and the chemical preference between different atoms in each alloy. The competition resulted in that the Al-centred clusters expand, i.e., with larger bond length, while the solute atom-centred clusters contract, so with the reduced bond length. (3) At the short-range scale, the structural heterogeneities were characterised by the co-existence and growth of the icosahedra-like (ICO-like) and crystal-like structures. During cooling, the Fe atoms show a higher degree of crystallinity than other atoms in the liquids. At the onset of crystal nucleation, relative percentage of the Fe-centred ICO-like and crystal-like Voronoi polyhedrons (VPs) reaches 8-10%, and the others in the range of 5.8-8.5%. (4) The Fe-centred short-range orders (SROs) tend to connect together via five different modes to form larger Fe-centred medium-range orders (MROs). The percentage of the face-sharing increases almost linearly as the temperature is cooled down, approximately 18-20% at the onset of nucleation in the 3 melts. The Fe-centred MROs gradually approach to the structures of the Al13Fe4 primary phase (monoclinic structure) and are proved to be the nucleation precursors for the Al13Fe4 phases. (5) For the quaternary Al-Cu-Fe-Si alloy melt, the research found that the liquid first transfers into a quasicrystal-like, metastable monoclinic Al13Fe4 phase. Such primary phase was confirmed to have a higher degree of five-fold and crystalline symmetry than the liquid. Upon cooling, the Fe-centred five-fold and crystalline symmetry both get enhanced in liquid, leading to a smaller Al13Fe4-liquid configuration entropy difference and interfacial free energy
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