DH-GAN: a physics-driven untrained generative adversarial network for holographic imaging

Untrained physics-driven aberration retrieval network

In the field of coherent diffraction imaging, phase retrieval is essential for correcting the aberration of an optic system. For estimating aberration from intensity, conventional methods rely on neural networks whose performance is limited by training datasets. In this Letter, we propose an untrained physics-driven aberration retrieval network (uPD-ARNet). It only uses one intensity image and iterates in a self-supervised way. This model consists of two parts: an untrained neural network and a forward physical model for the diffraction of the light field. This physical model can adjust the output of the untrained neural network, which can characterize the inverse process from the intensity to the aberration. The experiments support that our method is superior to other conventional methods for aberration retrieval.

Open-access database for digital lensless holographic microscopy and its application on the improvement of deep-learning-based autofocusing models

Among modern optical microscopy techniques, digital lensless holographic microscopy (DLHM) is one of the simplest label-free coherent imaging approaches. However, the hardware simplicity provided by the lensless configuration is often offset by the demanding computational postprocessing required to match the retrieved sample information to the user's expectations. A promising avenue to simplify this stage is the integration of artificial intelligence and machine learning (ML) solutions into the DLHM workflow. The biggest challenge to do so is the preparation of an extensive and high-quality experimental dataset of curated DLHM recordings to train ML models. In this work, a diverse, open-access dataset of DLHM recordings is presented as support for future research, contributing to the data needs of the applied research community. The database comprises 11,760 experimental DLHM holograms of bio and non-bio samples with diversity on the main recording parameters of the DLHM architecture. The database is divided into two datasets of 10 independent imaged samples. The first group, named multi-wavelength dataset, includes 8160 holograms and was recorded using laser diodes emitting at 654 nm, 510 nm, and 405 nm; the second group, named single-wavelength dataset, is composed of 3600 recordings and was acquired using a 633 nm He-Ne laser. All the experimental parameters related to the dataset acquisition, preparation, and calibration are described in this paper. The advantages of this large dataset are validated by re-training an existing autofocusing model for DLHM and as the training set for a simpler architecture that achieves comparable performance, proving its feasibility for improving existing ML-based models and the development of new ones.

Dual-constrained physics-enhanced untrained neural network for lensless imaging

An untrained neural network (UNN) paves a new way to realize lensless imaging from single-frame intensity data. Based on the physics engine, such methods utilize the smoothness property of a convolutional kernel and provide an iterative self-supervised learning framework to release the needs of an end-to-end training scheme with a large dataset. However, the intrinsic overfitting problem of UNN is a challenging issue for stable and robust reconstruction. To address it, we model the phase retrieval problem into a dual-constrained untrained network, in which a phase-amplitude alternating optimization framework is designed to split the intensity-to-phase problem into two tasks: phase and amplitude optimization. In the process of phase optimization, we combine a deep image prior with a total variation prior to retrain the loss function for the phase update. In the process of amplitude optimization, a total variation denoising-based Wirtinger gradient descent method is constructed to form an amplitude constraint. Alternative iterations of the two tasks result in high-performance wavefield reconstruction. Experimental results demonstrate the superiority of our method.

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.

Field recovery from digital inline holographic images of composite propellant combustion base on denoising diffusion model

Digital inline holography has gained extensive application in the optical diagnosis of solid propellant combustion. However, this method confronts several challenges. Firstly, the calculation time required for reconstruction and depth of field extension is excessively long. Secondly, the excessive smoke, airflow, and flame during combustion cause significant interference and poor reconstruction quality, which reduces the accuracy of particle identification. To address these issues, we have developed a holographic image reconstruction technique for aluminum particle combustion based on the Attention Mechanism, U-net, and Diffusion models. This approach enables end-to-end reconstruction of aluminum particle combustion holographic images, while effectively circumventing the interference of airflow combustion and flame.