46 research outputs found
Computational Imaging with Limited Photon Budget
The capability of retrieving the image/signal of interest from extremely low photon flux is attractive in scientific, industrial, and medical imaging applications. Conventional imaging modalities and reconstruction algorithms rely on hundreds to thousands of photons per pixel (or per measurement) to ensure enough signal-to-noise (SNR) ratio for extracting the image/signal of interest. Unfortunately, the potential of radiation or photon damage prohibits high SNR measurements in dose-sensitive diagnosis scenarios. In addition, imaging systems utilizing inherently weak signals as contrast mechanism, such as X-ray scattering-based tomography, or attosecond pulse retrieval from the streaking trace, entail prolonged integration time to acquire hundreds of photons, thus rendering high SNR measurement impractical. This dissertation addresses the problem of imaging from limited photon budget when high SNR measurements are either prohibitive or impractical. A statistical image reconstruction framework based on the knowledge of the image-formation process and the noise model of the measurement system has been constructed and successfully demonstrated on two imaging platforms – photon-counting X-ray imaging, and attosecond pulse retrieval. For photon-counting X-ray imaging, the statistical image reconstruction framework achieves high-fidelity X-ray projection and tomographic image reconstruction from as low as 16 photons per pixel on average. The capability of our framework in modeling the reconstruction error opens the opportunity of designing the optimal strategies to distribute a fixed photon budget for region-of-interest (ROI) reconstruction, paving the way for radiation dose management in an imaging-specific task. For attosecond pulse retrieval, a learning-based framework has been incorporated into the statistical image reconstruction to retrieve the attosecond pulses from the noisy streaking traces. Quantitative study on the required signal-to-noise ratio for satisfactory pulse retrieval enabled by our framework provides a guideline to future attosecond streaking experiments. In addition, resolving the ambiguities in the streaking process due to the carrier envelop phase has also been demonstrated with our statistical reconstruction framework
Few-photon computed x-ray imaging
X-ray is a ubiquitous imaging modality in clinical diagnostics and industrial
inspections, thanks to its high penetration power. Conventional x-ray imaging
system, equipped with energy-integrating detectors, collects approximately 1000
to 10000 counts per pixel to ensure sufficient signal to noise ratio (SNR). The
recent development of energy sensitive photon counting detectors opens new
possibilities for x-ray imaging at low photon flux. In this letter, we report a
novel photon-counting scheme that records the time stamp of individual photons,
which follows a negative binomial distribution, and demonstrated the
reconstruction based on the few-photon statistics. The projection and
tomography reconstruction from measurements of roughly 10 photons shows the
potential of using photon counting detectors for dose-efficient x-ray imaging
systems.Comment: Revised manuscrip
A physical neural network training approach toward multi-plane light conversion design
Multi-plane light converter (MPLC) designs supporting hundreds of modes are
attractive in high-throughput optical communications. These photonic structures
typically comprise >10 phase masks in free space, with millions of independent
design parameters. Conventional MPLC design using wavefront matching updates
one mask at a time while fixing the rest. Here we construct a physical neural
network (PNN) to model the light propagation and phase modulation in MPLC,
providing access to the entire parameter set for optimization, including not
only profiles of the phase masks and the distances between them. PNN training
supports flexible optimization sequences and is a superset of existing MPLC
design methods. In addition, our method allows tuning of hyperparameters of PNN
training such as learning rate and batch size. Because PNN-based MPLC is found
to be insensitive to the number of input and target modes in each training
step, we have demonstrated a high-order MPLC design (45 modes) using mini
batches that fit into the available computing resources.Comment: Draft for submission to Optics Expres
SRIBO: An Efficient and Resilient Single-Range and Inertia Based Odometry for Flying Robots
Positioning with one inertial measurement unit and one ranging sensor is
commonly thought to be feasible only when trajectories are in certain patterns
ensuring observability. For this reason, to pursue observable patterns, it is
required either exciting the trajectory or searching key nodes in a long
interval, which is commonly highly nonlinear and may also lack resilience.
Therefore, such a positioning approach is still not widely accepted in
real-world applications. To address this issue, this work first investigates
the dissipative nature of flying robots considering aerial drag effects and
re-formulates the corresponding positioning problem, which guarantees
observability almost surely. On this basis, a dimension-reduced wriggling
estimator is proposed accordingly. This estimator slides the estimation horizon
in a stepping manner, and output matrices can be approximately evaluated based
on the historical estimation sequence. The computational complexity is then
further reduced via a dimension-reduction approach using polynomial fittings.
In this way, the states of robots can be estimated via linear programming in a
sufficiently long interval, and the degree of observability is thereby further
enhanced because an adequate redundancy of measurements is available for each
estimation. Subsequently, the estimator's convergence and numerical stability
are proven theoretically. Finally, both indoor and outdoor experiments verify
that the proposed estimator can achieve decimeter-level precision at hundreds
of hertz per second, and it is resilient to sensors' failures. Hopefully, this
study can provide a new practical approach for self-localization as well as
relative positioning of cooperative agents with low-cost and lightweight
sensors
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Machine Unlearning (MU) algorithms have become increasingly critical due to
the imperative adherence to data privacy regulations. The primary objective of
MU is to erase the influence of specific data samples on a given model without
the need to retrain it from scratch. Accordingly, existing methods focus on
maximizing user privacy protection. However, there are different degrees of
privacy regulations for each real-world web-based application. Exploring the
full spectrum of trade-offs between privacy, model utility, and runtime
efficiency is critical for practical unlearning scenarios. Furthermore,
designing the MU algorithm with simple control of the aforementioned trade-off
is desirable but challenging due to the inherent complex interaction. To
address the challenges, we present Controllable Machine Unlearning (ConMU), a
novel framework designed to facilitate the calibration of MU. The ConMU
framework contains three integral modules: an important data selection module
that reconciles the runtime efficiency and model generalization, a progressive
Gaussian mechanism module that balances privacy and model generalization, and
an unlearning proxy that controls the trade-offs between privacy and runtime
efficiency. Comprehensive experiments on various benchmark datasets have
demonstrated the robust adaptability of our control mechanism and its
superiority over established unlearning methods. ConMU explores the full
spectrum of the Privacy-Utility-Efficiency trade-off and allows practitioners
to account for different real-world regulations. Source code available at:
https://github.com/guangyaodou/ConMU
Domain Generalization with Fourier Transform and Soft Thresholding
Domain generalization aims to train models on multiple source domains so that
they can generalize well to unseen target domains. Among many domain
generalization methods, Fourier-transform-based domain generalization methods
have gained popularity primarily because they exploit the power of Fourier
transformation to capture essential patterns and regularities in the data,
making the model more robust to domain shifts. The mainstream
Fourier-transform-based domain generalization swaps the Fourier amplitude
spectrum while preserving the phase spectrum between the source and the target
images. However, it neglects background interference in the amplitude spectrum.
To overcome this limitation, we introduce a soft-thresholding function in the
Fourier domain. We apply this newly designed algorithm to retinal fundus image
segmentation, which is important for diagnosing ocular diseases but the neural
network's performance can degrade across different sources due to domain
shifts. The proposed technique basically enhances fundus image augmentation by
eliminating small values in the Fourier domain and providing better
generalization. The innovative nature of the soft thresholding fused with
Fourier-transform-based domain generalization improves neural network models'
performance by reducing the target images' background interference
significantly. Experiments on public data validate our approach's effectiveness
over conventional and state-of-the-art methods with superior segmentation
metrics