Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for applications involving radiation-sensitive samples. However, most PR methods struggle in low-dose scenarios due to high shot noise. Recent advancements in optical data acquisition setups, such as in-situ CDI, have shown promise for low-dose imaging, but they rely on a time series of measurements, making them unsuitable for single-image applications. Similarly, data-driven phase retrieval techniques are not easily adaptable to data-scarce situations. Zero-shot deep learning methods based on pre-trained and implicit generative priors have been effective in various imaging tasks but have shown limited success in PR. In this work, we propose low-dose deep image prior (LoDIP), which combines in-situ CDI with the power of implicit generative priors to address single-image low-dose phase retrieval. Quantitative evaluations demonstrate LoDIP's superior performance in this task and its applicability to real experimental scenarios.

We develop two new algorithms for tomographic reconstruction which incorporate the technique of equally-sloped tomography (EST) and allow for the optimized and flexible implementation of regularization schemes, such as total variation constraints, and the incorporation of arbitrary physical constraints. The founding structure of the developed algorithms is EST, a technique of tomographic acquisition and reconstruction first proposed by Miao in 2005 for performing tomographic image reconstructions from a limited number of noisy projections in an accurate manner by avoiding direct interpolations. EST has recently been successfully applied to coherent diffraction microscopy, electron microscopy, and computed tomography for image enhancement and radiation dose reduction. However, the bottleneck of EST lies in its slow speed due to its higher computation requirements. In this paper, we formulate the EST approach as a constrained problem and subsequently transform it into a series of linear problems, which can be accurately solved by the operator splitting method. Based on these mathematical formulations, we develop two iterative algorithms for tomographic image reconstructions through EST, which incorporate Bregman and continuative regularization. Our numerical experiment results indicate that the new tomographic image reconstruction algorithms not only significantly reduce the computational time, but also improve the image quality. We anticipate that EST coupled with the novel iterative algorithms will find broad applications in X-ray tomography, electron microscopy, coherent diffraction microscopy, and other tomography fields.

We describe an optimal discriminant feature based pattern recognition system with two-layer feedforward neural networks. This system can simplify the structure of the covariance matrix to facilitate visual interpretation of cluster graphs and to assess clustering performance. It also can enable one class to be well concentrated and an other maximally spread over the space, while keeping the inter-cluster distance as large as possible. The performance of our 2-D optimal discriminant feature-based neural network system does not degrade compared with a multidimensional feature-based neural network system, while the computational complexity and the storage requirements are significantly improved. This technique has been applied and has shown accurate classification (98% in probability) of digital waveforms obtained from digitized radiation detector outputs. To the best of our knowledge, application of such a technique is novel.

We have addressed the problem of analyzing the digital pulse waveforms of radiation detector outputs. With the availability of extremely high-speed A/D conversion with good resolution, it is now possible to look more deeply at the waveform shapes than is currently done. In our studies, a new technique of unsupervised pattern recognition has been applied which has demonstrated accurate classification (98.33% in probability) of digital pulse waveforms. To the best of our knowledge, application of such a technique is novel. The preliminary results of this system, which show clearly improved measurement conditions, are therefore very promising.