The Gerchberg–Saxton (GS) algorithm, which retrieves phase information from the measured intensities on two related planes (the source plane and the target plane), has been widely adopted in a variety of applications when holographic methods are challenging to be implemented. In this work, we showed that the GS algorithm can be generalized to retrieve the unknown propagating function that connects these two planes. As a proof-of-concept, we employed the generalized GS (GGS) algorithm to retrieve the optical transmission matrix (TM) of a complex medium through the measured intensity distributions on the target plane. Numerical studies indicate that the GGS algorithm can efficiently retrieve the optical TM while maintaining accuracy. With the same training data set, the computational time cost by the GGS algorithm is orders of magnitude less than that consumed by other non-holographic methods reported in the literature. Besides numerical investigations, we also experimentally demonstrated retrieving the optical TMs of a stack of ground glasses and a 1-m-long multimode fiber using the GGS algorithm. The accuracy of the retrieved TM was evaluated by synthesizing high-quality single foci and multiple foci on the target plane through these complex media. These results indicate that the GGS algorithm can handle a large TM with high efficiency, showing great promise in a variety of applications in optics.
Characterizing the transmission matrix (TM) of a multimode fiber (MMF) benefits many fiber-based applications and allows in-depth studies on the physical properties. For example, by modulating the incident field, the knowledge of the TM allows one to synthesize any optical field at the distill end of the MMF. However, the extraction of optical fields usually requires holographic measurements with interferometry, which complicates the system design and introduces additional noise. In this work, we developed an efficient method to retrieve the TM of the MMF in a referenceless optical system. With pure intensity measurements, this method uses the extended Kalman filter (EKF) to recursively search for the optimum solution. To facilitate the computational process, a modified speckle-correlation scatter matrix (MSSM) is constructed as a low-fidelity initial estimation. This method, termed EKF-MSSM, only requires 4N intensity measurements to precisely solve for N unknown complex variables in the TM. Experimentally, we successfully retrieved the TM of the MMF with high precision, which allows optical focusing with the enhancement (>70%) close to the theoretical value. We anticipate that this method will serve as a useful tool for studying physical properties of the MMFs and potentially open new possibilities in a variety of applications in fiber optics.
Feedback-based wavefront shaping focuses light through scattering media by employing phase optimization algorithms. Genetic algorithms (GAs), inspired by the process of natural selection, are well suited for phase optimization in wavefront shaping problems. In 2012, Conkey et al. first introduced a GA into feedback-based wavefront shaping to find the optimum phase map. Since then, due to its superior performance in noisy environment, the GA has been widely adopted by lots of implementations. However, there have been limited studies discussing and optimizing the detailed procedures of the GA. To fill this blank, in this study, we performed a thorough study on the performance of the GA for focusing light through scattering media. Using numerical tools, we evaluated certain procedures that can be potentially improved and provided guidance on how to choose certain parameters appropriately. This study is beneficial in improving the performance of wavefront shaping systems with GAs.
Single-pixel holography (SPH) is capable of generating holographic images with rich spatial information by employing only a single-pixel detector. Thanks to the relatively low dark-noise production, high sensitivity, large bandwidth, and cheap price of single-pixel detectors in comparison to pixel-array detectors, SPH is becoming an attractive imaging modality at wavelengths where pixel-array detectors are not available or prohibitively expensive. In this work, we develop a high-throughput single-pixel compressive holography with a space-bandwidth-time product (SBP-T) of 41,667 pixels/s, realized by enabling phase stepping naturally in time and abandoning the need for phase-encoded illumination. This holographic system is scalable to provide either a large field of view (~83 mm2) or a high resolution (5.80 μm × 4.31 μm). In particular, high-resolution holographic images of biological tissues are presented, exhibiting rich contrast in both amplitude and phase. This work is an important step towards multi-spectrum imaging using a single-pixel detector in biophotonics.
Multimode fibers (MMFs) are emerging as promising transmission media for delivering images. However, strong mode coupling inherent in MMFs induces difficulties in directly projecting two-dimensional images through MMFs. By training two subnetworks named Actor-net and Model-net synergetically, [Nature Machine Intelligence 2, 403 (2020)10.1038/s42256-020-0199-9] alleviated this issue and demonstrated projecting images through MMFs with high fidelity. In this work, we make a step further by improving the generalization ability to greyscale images. The modified projector network contains three subnetworks, namely forward-net, backward-net, and holography-net, accounting for forward propagation, backward propagation, and the phase-retrieval process. As a proof of concept, we experimentally trained the projector network using randomly generated phase maps and their corresponding resultant speckle images output from a 1-meter-long MMF. With the network being trained, we successfully demonstrated projecting binary images from MNIST and EMNIST and greyscale images from Fashion-MNIST, exhibiting averaged Pearson’s correlation coefficients of 0.91, 0.92, and 0.87, respectively. Since all these projected images have never been seen by the projector network before, a strong generalization ability in projecting greyscale images is confirmed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.