arXiv subjects

Zichao Di

Publications and source records attributed to Zichao Di.

6 recordsLinked to original sources

arXiv2025

Optimizing Paths for Adaptive Fly-Scan Microscopy: An Extended Version

In x-ray microscopy, traditional raster-scanning techniques are used to acquire a microscopic image in a series of step-scans. Alternatively, scanning the x-ray probe along a continuous path, called a fly-scan, reduces scan time and increases scan efficiency. However, not all regions of an image are equally important. Currently used fly-scan methods do not adapt to the characteristics of the sample during the scan, often wasting time in uniform, uninteresting regions. One approach to avoid unnecessary scanning in uniform regions for raster step-scans is to use deep learning techniques to select a shorter optimal scan path instead of a traditional raster scan path, followed by reconstructing the entire image from the partially scanned data. However, this approach heavily depends on the quality of the initial sampling, requires a large dataset for training, and incurs high computational costs. We propose leveraging the fly-scan method along an optimal scanning path, focusing on regions of interest (ROIs) and using image completion techniques to reconstruct details in non-scanned areas. This approach further shortens the scanning process and potentially decreases x-ray exposure dose while maintaining high-quality and detailed information in critical regions. To achieve this, we introduce a multi-iteration fly-scan framework that adapts to the scanned image. Specifically, in each iteration, we define two key functions: (1) a score function to generate initial anchor points and identify potential ROIs, and (2) an objective function to optimize the anchor points for convergence to an optimal set. Using these anchor points, we compute the shortest scanning path between optimized anchor points, perform the fly-scan, and subsequently apply image completion based on the acquired information in preparation for the next scan iteration.

eess.IV↗

arXiv2025

Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction

Low-rank tensor estimation offers a powerful approach to addressing high-dimensional data challenges and can substantially improve solutions to ill-posed inverse problems, such as image reconstruction under noisy or undersampled conditions. Meanwhile, tensor decomposition has gained prominence in federated learning (FL) due to its effectiveness in exploiting latent space structure and its capacity to enhance communication efficiency. In this paper, we present a federated image reconstruction method that applies Tucker decomposition, incorporating joint factorization and randomized sketching to manage large-scale, multimodal data. Our approach avoids reconstructing full-size tensors and supports heterogeneous ranks, allowing clients to select personalized decomposition ranks based on prior knowledge or communication capacity. Numerical results demonstrate that our method achieves superior reconstruction quality and communication compression compared to existing approaches, thereby highlighting its potential for multimodal inverse problems in the FL setting.

cs.LG↗

arXiv2024

A Stochastic ADMM Algorithm for Large-Scale Ptychography with Weighted Difference of Anisotropic and Isotropic Total Variation

Ptychography, a prevalent imaging technique in fields such as biology and optics, poses substantial challenges in its reconstruction process, characterized by nonconvexity and large-scale requirements. This paper presents a novel approach by introducing a class of variational models that incorporate the weighted difference of anisotropic--isotropic total variation. This formulation enables the handling of measurements corrupted by Gaussian or Poisson noise, effectively addressing the nonconvex challenge. To tackle the large-scale nature of the problem, we propose an efficient stochastic alternating direction method of multipliers, which guarantees convergence under mild conditions. Numerical experiments validate the superiority of our approach by demonstrating its capability to successfully reconstruct complex-valued images, especially in recovering the phase components even in the presence of highly corrupted measurements.

math.NA↗

arXiv2023

Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy

With the continuing advances in scientific instrumentation, scanning microscopes are now able to image physical systems with up to sub-atomic-level spatial resolutions and sub-picosecond time resolutions. Commensurately, they are generating ever-increasing volumes of data, storing and analysis of which is becoming an increasingly difficult prospect. One approach to address this challenge is through self-driving experimentation techniques that can actively analyze the data being collected and use this information to make on-the-fly measurement choices, such that the data collected is sparse but representative of the sample and sufficiently informative. Here, we report the Fast Autonomous Scanning Toolkit (FAST) that combines a trained neural network, a route optimization technique, and efficient hardware control methods to enable a self-driving scanning microscopy experiment. The key features of our method are that: it does not require any prior information about the sample, it has a very low computational cost, and that it uses generic hardware controls with minimal experiment-specific wrapping. We test this toolkit in numerical experiments and a scanning dark-field x-ray microscopy experiment of a $WSe_2$ thin film, where our experiments show that a FAST scan of <25% of the sample is sufficient to produce both a high-fidelity image and a quantitative analysis of the surface distortions in the sample. We show that FAST can autonomously identify all features of interest in the sample while significantly reducing the scan time, the volume of data acquired, and dose on the sample. The FAST toolkit is easy to apply for any scanning microscopy modalities and we anticipate adoption of this technique will empower broader multi-level studies of the evolution of physical phenomena with respect to time, temperature, or other experimental parameters.

physics.app-ph↗

arXiv2019

Bilevel Optimization, Deep Learning and Fractional Laplacian Regularization with Applications in Tomography

In this work we consider a generalized bilevel optimization framework for solving inverse problems. We introduce fractional Laplacian as a regularizer to improve the reconstruction quality, and compare it with the total variation regularization. We emphasize that the key advantage of using fractional Laplacian as a regularizer is that it leads to a linear operator, as opposed to the total variation regularization which results in a nonlinear degenerate operator. Inspired by residual neural networks, to learn the optimal strength of regularization and the exponent of fractional Laplacian, we develop a dedicated bilevel optimization neural network with a variable depth for a general regularized inverse problem. We also draw some parallels between an activation function in a neural network and regularization. We illustrate how to incorporate various regularizer choices into our proposed network. As an example, we consider tomographic reconstruction as a model problem and show an improvement in reconstruction quality, especially for limited data, via fractional Laplacian regularization. We successfully learn the regularization strength and the fractional exponent via our proposed bilevel optimization neural network. We observe that the fractional Laplacian regularization outperforms total variation regularization. This is specially encouraging, and important, in the case of limited and noisy data.

eess.IV↗

arXiv2018

Multigrid Optimization for Large-Scale Ptychographic Phase Retrieval

Ptychography is a popular imaging technique that combines diffractive imaging with scanning microscopy. The technique consists of a coherent beam that is scanned across an object in a series of overlapping positions, leading to reliable and improved reconstructions. Ptychographic microscopes allow for large fields to be imaged at high resolution at the cost of additional computational expense. In this work, we propose a multigrid-based optimization framework to reduce the computational burdens of large-scale ptychographic phase retrieval. Our proposed method exploits the inherent hierarchical structures in ptychography through tailored restriction and prolongation operators for the object and data domains. Our numerical results show that our proposed scheme accelerates the convergence of its underlying solver and outperforms the Ptychographic Iterative Engine (PIE), a workhorse in the optics community.

math.NA↗