Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging

We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We show that this illumination coding scheme is highly scalable in achieving flexible resolution, and robust to experimental variations. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5X resolution enhancement across 4X FOVs using only five multiplexed measurements -- more than 10X data reduction over the state-of-the-art. Typical DL algorithms tend to provide over-confident predictions, whose errors are only discovered in hindsight. We develop an uncertainty learning framework to overcome this limitation and provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and "out-of-distribution" testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable AI-augmented large-SBP phase imaging with dependable predictions.Published versio

Reconstructing the Scattering Matrix from Scanning Electron Diffraction Measurements Alone

Three-dimensional phase contrast imaging of multiply-scattering samples in X-ray and electron microscopy is extremely challenging, due to small numerical apertures, the unavailability of wavefront shaping optics, and the highly nonlinear inversion required from intensity-only measurements. In this work, we present a new algorithm using the scattering matrix formalism to solve the scattering from a non-crystalline medium from scanning diffraction measurements, and recover the illumination aberrations. Our method will enable 3D imaging and materials characterization at high resolution for a wide range of materials

Analysis and development of phase retrieval algorithms for ptychography

Ptychography, a relatively new form of phase retrieval, can reconstruct both intensity and phase images of a sample from a group of diffraction patterns, which are recorded as the sample is translated through a grid of positions. To recover the phase information lost in the recording of these diffraction patterns, iterative algorithms must optimise an objective function full of local minima, in a huge multidimensional space. Many such algorithms have been developed, each aiming to converge rapidly whilst avoiding stagnation. This thesis aims to set a standard error metric for comparing some of the more popular algorithms, to determine their advantages and disadvantages under a range of different conditions, and hence develop a more adaptive algorithm that combines the advantages of these ancestors. In this thesis, different algorithms are explained together with their reconstruction results from both simulated and practical data. Modifications for mPIE, ADMM and RAAR are suggested to either reducing the number of parameters or improving their computation efficiency. An improved spatial error metric, which can evaluate the reconstruction quality by removing inherent ambiguities, is introduced to compare these algorithms. Based on the explained phase retrieval algorithms, a new algorithm, i.e., adaptive PIE, is developed. It has。 a faster converging speed and better accuracy comparing to its ancestors