Traditional numerical reconstruction methods in digital holography are faced with problems such as inaccurate and time-consuming unwrapping or the need to capture multiple holograms with different diffraction distances. In recent years, deep learning, as a new and effective optimization tool, has been widely used in digital holography. However, most supervised deep learning methods require large-scale paired data, and their preparation is time-consuming and laborious. Here, we propose a new deep learning approach that can use less unpaired data to train neural networks, thereby reducing the need for labeled data. This method can reconstruct complex amplitudes for holographic reconstruction and generate synthetic holograms at the same time. The reconstructed complex amplitudes have higher image quality, while the generated holograms can reconstruct the complex amplitudes successfully
A deep neural network with differential architecture is proposed to reconstruct turbulence phases from single-shot intensity images of extended targets, and a phase spatial modulator (SLM) is used for wavefront correction. The neural network takes the inputs of an aberrated intensity image and a background image and separates the turbulence phase from the target structure by comparing the high-dimensional features of the two inputs. The SLM is used to simulate atmospheric turbulence in the optical path and for correction of the external turbulent field. This method is verified in the optical path with the flame field.
Water scattering is a significant limiting factor for underwater imaging quality. It changes the transportation direction of the original light path, causes the attenuation of light intensity, and so on. In this work, we use a synthetic polarizing camera to capture the images with different polarization states and reduce the impact of water scattering in one step with the underwater light propagation model and the Stokes vector. In addition, an untrained deep network is designed to complete the image descattering processing. Compared with the methods based on deep learning or physical model prior, it is more efficient. This technology is suitable for use in portable underwater imaging optical systems for real-time imaging and detecting particulate matter such as microplastics and microbial particles. It also broadens the application of underwater polarization imaging.
Digital holography records the entire wavefront of an object, including amplitude and phase. To reconstruct the object numerically, we can backpropagate the hologram with Fresnel–Kirchhoff integral-based algorithms such as the angular spectrum method and the convolution method. Although effective, these techniques require prior knowledge, such as the object distance, the incident angle between the two beams, and the source wavelength. Undesirable zero-order and twin images have to be removed by an additional filtering operation, which is usually manual and consumes more time in off-axis configuration. In addition, for phase imaging, the phase aberration has to be compensated, and subsequently an unwrapping step is needed to recover the true object thickness. The former either requires additional hardware or strong assumptions, whereas the phase unwrapping algorithms are often sensitive to noise and distortion. Furthermore, for a multisectional object, an all-in-focus image and depth map are desired for many applications, but current approaches tend to be computationally demanding. We propose an end-to-end deep learning framework, called a holographic reconstruction network, to tackle these holographic reconstruction problems. Through this data-driven approach, we show that it is possible to reconstruct a noise-free image that does not require any prior knowledge and can handle phase imaging as well as depth map generation.
In digital holography, it is critical to know the distance in order to reconstruct the multi-sectional object. This autofocusing is traditionally solved by reconstructing a stack of in-focus and out-of-focus images and using some focus metric, such as entropy or variance, to calculate the sharpness of each reconstructed image. Then the distance corresponding to the sharpest image is determined as the focal position. This method is effective but computationally demanding and time-consuming. To get an accurate estimation, one has to reconstruct many images. Sometimes after a coarse search, a refinement is needed. To overcome this problem in autofocusing, we propose to use deep learning, i.e., a convolutional neural network (CNN), to solve this problem. Autofocusing is viewed as a classification problem, in which the true distance is transferred as a label. To estimate the distance is equated to labeling a hologram correctly. To train such an algorithm, totally 1000 holograms are captured under the same environment, i.e., exposure time, incident angle, object, except the distance. There are 5 labels corresponding to 5 distances. These data are randomly split into three datasets to train, validate and test a CNN network. Experimental results show that the trained network is capable of predicting the distance without reconstructing or knowing any physical parameters about the setup. The prediction time using this method is far less than traditional autofocusing methods.
KEYWORDS: Holography, Digital holography, Microscopy, Super resolution, Optical scanning, Holograms, Diffraction, 3D image reconstruction, Imaging systems, Optical transfer functions
As a specific digital holographic microscopy system, optical scanning holography (OSH) is an appealing technique that makes use of the advantages of holography in the application of optical microscopy. In OSH system, a three-dimensional object is scanned with a Fresnel zone plate in a raster fashion, and the electrical signals are demodulated into a complex hologram by heterodyne detection. Then the recorded light wavefront information contained in the hologram allows one to digitally reconstruct the specimen for multiple purposes such as optical sectioning, extended focused imaging as well as three-dimensional imaging. According to Abbe criterion, however, akin to those conventional microscopic imaging systems, OSH suffers from limited resolving power due to the finite sizes of the objective lens and the aperture, i.e., low numerical aperture. To bypass the diffraction barrier in light microscopy, various super-resolution imaging techniques have been proposed. Among those methods, structured illumination is an ensemble imaging concept that modulates the spatial frequency by projecting additional well-defined patterns with different orientation and phase shift onto the specimen. Computational algorithms are then applied to remove the effect of the structure and to reconstruct a super-resolved image beyond the diffraction-limit. In this paper, we introduce this technique in OSH system to scale down the spatial resolution beyond the diffraction limit. The performance of the proposed method is validated by simulation and experimental results.
KEYWORDS: Holograms, Holography, Digital micromirror devices, 3D image reconstruction, 3D image processing, Diffraction, Distortion, 3D displays, Spherical lenses, Reconstruction algorithms
A digital micro-mirror device (DMD) acting as a real-time hologram is an emerging technology in dynamic holographic projection. This paper presents a lensless image magnification method in DMD holography by using a Fresnel hologram. By analyzing the diffraction order distribution in the image plane of a hologram produced by DMD, we find the factors that limit the size of the magnified image. We perform a lensless magnification experiment that shows good magnified images in accordance with the numerical results. Finally, we discuss methods to eliminate longitudinal error and chromatic aberration in three-dimensional (3-D) and color projection, respectively, and present a 3-D image reconstruction result that shows lensless magnification of a 3-D image without distortion. It is believed that this technique can be used in future real-time holographic projection based on digital light processing technology.
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