A fast and non-iterative method for generating a phase-only hologram, hereafter referred to as the patterned-phase-only hologram (PPOH), is reported in this paper. Briefly, a phase mask with a ...periodic phase pattern is added to the source image, and converted into a hologram. Subsequently, only the phase component is retained as a phase-only hologram. Experimental evaluation reveals that the visual quality of the reconstructed images of the PPOH generated with our proposed method is favorable, and superior to that obtained with existing methods.
We report a novel and fast method for converting a digital, complex Fresnel hologram into a phase-only hologram. Briefly, the pixels in the complex hologram are scanned sequentially in a row by row ...manner. The odd and even rows are scanned from opposite directions, constituting to a bidirectional error diffusion process. The magnitude of each visited pixel is forced to be a constant value, while preserving the exact phase value. The resulting error is diffused to the neighboring pixels that have not been visited before. The resulting novel phase-only hologram is called the bidirectional error diffusion (BERD) hologram. The reconstructed image from the BERD hologram exhibits high fidelity as compared with those obtained with the original complex hologram.
A sampled phase-only hologram (SPOH) is the phase component of the hologram of an object image with pixels being sampled with a periodic grid-cross pattern. The reconstructed image of a SPOH is a ...sparse image with abundant empty voids and degradation in sharpness and contrast. In this paper we proposed a method based on a new sampling scheme, together with stochastic binary search (SBS), to obtain an optimal sampling lattice that can be applied to generate phase-only holograms with enhanced reconstructed image. Experimental results show that with our proposed method, the fidelity and quality of the reconstructed image are increased.
Neutrinoless double-beta decay is a forbidden, lepton-number-violating nuclear transition whose observation would have fundamental implications for neutrino physics, theories beyond the Standard ...Model, and cosmology. In this review, we summarize the theoretical progress to understand this process, the expectations and implications under various particle physics models, and the nuclear physics challenges that affect the precise predictions of the decay half-life. We also provide a synopsis of the current and future large-scale experiments that aim to discover this process in physically well-motivated half-life ranges.
Past research has demonstrated that a digital, complex Fresnel hologram can be converted into a phase-only hologram with the use of the bi-direction error diffusion (BERD) algorithm. However, the ...recursive nature error diffusion process is lengthy and increases monotonically with hologram size. In this paper, we propose a method to overcome this problem. Briefly, each row of a hologram is partitioned into short non-overlapping segments, and a localized error diffusion algorithm is applied to convert the pixels in each segment into phase only values. Subsequently, the error signal is redistributed with low-pass filtering. As the operation on each segment is independent of others, the conversion process can be conducted at high speed with the graphic processing unit. The hologram obtained with the proposed method, known as the Localized Error Diffusion and Redistribution (LERDR) hologram, is over two orders of magnitude faster than that obtained by BERD for a 2048×2048 hologram, exceeding the capability of generating quality phase-only holograms in video rate.
Abstract
Adaptive Optical Scanning Holography (AOSH) represents a powerful technique that employs an adaptive approach to selectively omit certain lines within holograms, guided by the utilization of ...Normalized-Mean-Error (NME) as a predictive measure. This approach effectively diminishes scanning time and conserves the storage space required for data preservation. However, there exists alternative methods superior to NME in terms of evaluating the model’s efficacy. This paper introduces two novel methods, namely Normalized-Root-Mean-Square-Error (NRMSE) and Normalized-Mean-Square-Error (NMSE), into the AOSH system, leading to the development of NRMSE-AOSH and NMSE-AOSH. These new systems aim to further minimize duration of holographic recording. Through a comparative analysis of hologram lines between the two newly proposed AOSH systems and the original AOSH, we demonstrate that both NRMSE-AOSH and NMSE-AOSH effectively reduce the number of hologram lines while maintaining the hologram’s informational content. Among the three methods, our two new methods exhibit better performance compared with the original method.
Recently, a method known as "ensemble deep learning invariant hologram classification" (EDL-IHC) for classifying of holograms of deformable objects with deep learning network (DLN) has been ...demonstrated. However DL-IHC requires substantial computational resources to attain near perfect success rate (≥99
). In practice, it is always desirable to have higher success rate with a low complexity DLN. In this paper we propose a low complexity DLN known as "ensemble deep learning invariant hologram classification" (EDL-IHC). In comparison with DL-IHC, our proposed hologram classifier has promoted the success rate by 2.86% in the classification of holograms of handwritten numerals.
We present a novel non-iterative method for generating phase-only Fresnel holograms. The intensity image of the source object scene is first down-sampled with uniform grid-cross lattices. A Fresnel ...hologram is then generated from the intensity and the depth information of the sampled object points. Subsequently, only the phase component of the hologram is preserved, resulting in a pure phase hologram that we call the sampled-phase-only hologram (SPOH). Experimental evaluation reveals that the numerical, as well as the optical reconstructed images of the proposed phase-only hologram derived with our method are of high visual quality. Moreover, the reconstructed optical image is brighter, and less affected by phase noise contamination on the hologram as compared with those generated with existing error-diffusion approaches.
Advancements in optical, computing, and electronic technologies have enabled holograms of physical three-dimensional (3D) objects to be captured. The hologram can be displayed with a spatial light ...modulator to reconstruct a visible image. Although holography is an ideal solution for recording 3D images, a hologram comprises high-frequency fringe patterns that are almost impossible to recognize with traditional computer vision methods. Recently, it has been shown that holograms can be classified with deep learning based on convolution neural networks. However, the method can only achieve a high success classification rate if the image represented in the hologram is without speckle noise and occlusion. Minor occlusion of the image generally leads to a substantial drop in the success rate. This paper proposes a method known as ensemble deep-learning invariant occluded hologram classification to overcome this problem. The proposed new method attains over 95% accuracy in the classification of holograms of partially occluded handwritten numbers contaminated with speckle noise. To achieve the performance, a new augmentation scheme and a new enhanced ensemble structure are necessary. The new augmentation process includes occluded objects and simulates the worst-case scenario of speckle noise.
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