Due to the loss of phase information in images captured by intensity-only measurements, the numerical reconstruction of inline digital holographic imaging suffers from the undesirable twin-image artifact. This artifact presents as an out-of-focus conjugate at the virtual image plane and reduces the reconstruction quality. In this work, we propose a diffusion-based generative model that eliminates such defocus noise in single-shot inline digital holography. The diffusion-based generative model learns the implicit prior of the underlying data distribution by progressively injecting random noise in data and then generating high-quality samples by reversing this process. Although the diffusion model has been successful in various challenging tasks in computer vision, its potential in scientific imaging has not been fully explored yet, and one challenge is the inherent randomness in its reverse sampling process. To address this issue, we incorporate the underlying physics of image formation as a prior, which constrains the possible samples from the data distribution. Specifically, we include an extra gradient correction step in each reverse sampling process to introduce data consistency and generate better results. We demonstrate the feasibility of our approach using simulated and experimental holograms and compare our results with previous methods. Our model recovers detailed object information and significantly suppresses the twin-image noise. The proposed method is explainable, generalizable, and transferable to other samples from various distributions, making it a promising tool for digital holographic reconstruction.
The assessment of microplastics (MPs) pollution and water quality monitoring raise a lot of attention in recent years. Discriminative methods are highly needed for quick and accurate in situ MP detections. Digital holography records the wavefront information of the objects and contains the morphology, refractive index, and roughness information. Polarization imaging inspects the optical anisotropy of MPs, which is related to their birefringence and material characteristics. In this work, we explore the capability of holographic and polarization imaging for the identification of MPs. The computed features, such as the angle of polarization (AoP) and degree of linear polarization (DoLP), show distinguishable characteristics of MPs. We inspect the method feasibility on MP classification as well as biological and natural particles. The proposed method shows potential use in real-time, non-contact in situ MPs detection and water pollution monitoring.
Dynamic speckle analysis (DSA) is a non-invasive method to detect movements of the inspected objects. By illuminating the observed sample using a coherent light source, motion information can be obtained from a series of reflecting speckle patterns. Conventional DSA methods record the intensity of the speckle patterns using a frame-based imaging sensor. Here, we propose a novel implementation of DSA using the event sensor which captures the brightness changes of the dynamic speckle patterns with high temporal resolution and low latency. Our method is based on block matching algorithm in which the captured event stream is divided into many non-overlapping blocks and motion information can be computed by searching for the most likely blocks. The experiment results demonstrate the feasibility of our proposed method in different dynamic levels and this work will be beneficial for various applications such as biomedical imaging and material science.
3D micro-particle field reconstruction with high accuracy and low latency is an ambitious and important task within various applications. Without using any focusing optics, the digital holography (DH) is used as a high throughput and compact imaging system to retrieve the particle 3D distribution information from two-dimensional(2D) gray-scale interference pattern. In this work, a one-stage digital in-line holography model is proposed with competitive performance in localisation accuracy and extraction rate with improved processing speed. This model facilitates the analysis of the dynamic displacements and motions for micro particles or cells and could be further extended to various types of computational imaging problems sharing the similar traits.
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