PS-NET: an end-to-end phase space depth estimation approach for computer-generated holograms.

In the present work, an end-to-end approach is proposed for recovering an RGB-D scene representation directly from a hologram using its phase space representation. The proposed method involves four steps. First, a set of silhouette images is extracted from the hologram phase space representation. Second, a minimal 3D volume that describes these silhouettes is extracted. Third, the extracted 3D volume is decomposed into horizontal slices, and each slice is processed using a neural network to generate a coarse estimation of the scene geometry. Finally, a third neural network is employed to refine the estimation for higher precision applications. Experimental results demonstrate that the proposed approach yields faster and more accurate results compared to numerical reconstruction-based methods. Moreover, the obtained RGB-D representation can be directly utilized for alternative applications such as motion estimation.

Automatic depth map retrieval from digital holograms using a deep learning approach.

Information extraction from computer-generated holograms using learning-based methods is a topic that has not received much research attention. In this article, we propose and study two learning-based methods to extract the depth information from a hologram and compare their performance with that of classical depth from focus (DFF) methods. We discuss the main characteristics of a hologram and how these characteristics can affect model training. The obtained results show that it is possible to extract depth information from a hologram if the problem formulation is well-posed. The proposed methods are faster and more accurate than state-of-the-art DFF methods.

H-Seg: a horizontal reconstruction volume segmentation method for accurate depth estimation in a computer-generated hologram.

In this work, we introduce a novel approach for depth estimation in a computer-generated hologram by employing horizontal segmentation of the reconstruction volume instead of conventional vertical segmentation. The reconstruction volume is divided into horizontal slices and each slice is processed using a residual U-net architecture to identify in-focus lines, enabling determination of the slice's intersection with the 3D scene. The individual slice results are then combined to generate a dense depth map of the scene. Our experiments demonstrate the effectiveness of our method, with improved accuracy, faster processing times, lower graphics processing unit (GPU) utilization, and smoother predicted depth maps than existing state-of-the-art models.

Automatic depth map retrieval from digital holograms using a depth-from-focus approach.

Recovering the scene depth map from a computer-generated hologram is a problem that remains unsolved, despite the growing interest in the subject. In this paper, we propose to study the application of depth-from-focus (DFF) methods to retrieve the depth information from the hologram. We discuss the different hyperparameters that are required for the application of the method and their impact on the final result. The obtained results show that DFF methods can be used for depth estimation from the hologram if the set of hyperparameters is well chosen.

JPEG Pleno holography presents the numerical reconstruction software for holograms: an excursion in holographic views.

Digital reconstructions of numerical holograms enable data visualization and serve a multitude of purposes ranging from microscopy to holographic displays. Over the years, many pipelines have been developed for specific hologram types. Within the standardization effort of JPEG Pleno holography, an open-source MATLAB toolbox was developed that reflects the best current consensus. It can process Fresnel, angular spectrum, and Fourier-Fresnel holograms with one or more color channels; it also allows for diffraction-limited numerical reconstructions. The latter provides a way to reconstruct holograms at their intrinsic physical instead of an arbitrarily chosen numerical resolution. The Numerical Reconstruction Software for Holograms v10 supports all large public data sets featured by UBI, BCOM, ETRI, and ETRO, in their native and vertical off-axis binary forms. Through the release of this software, we hope to improve the reproducibility of research, thus enabling consistent comparison of data between research groups and the quality of specific numerical reconstructions.

JPEG Pleno holography: scope and technology validation procedures.

JPEG Pleno is a standardization framework addressing the compression and signaling of plenoptic modalities. While the standardization of solutions to handle light field content is currently reaching its final stage, the Joint Photographic Experts Group (JPEG) committee is now preparing for the standardization of solutions targeting point cloud and holographic modalities. This paper addresses the challenges related to the standardization of compression technologies for holographic content and associated test methodologies.

Real-time layer-based computer-generated hologram calculation for the Fourier transform optical system.

With the growing interest for augmented reality devices, holography is often considered as a promising technology to overcome the focus issues of conventional stereoscopic displays. To enlarge the field of view of holographic head-mounted displays, a Fourier transform optical system (FTOS) has been proposed. However, since the scene geometry is distorted by the FTOS, it is necessary to compensate the position of each scene point during the hologram computation, resulting in long calculation times. In this paper, we propose a real-time computer-generated hologram calculation method for the FTOS. Whereas previously proposed methods used a ray-tracing approach to compensate the distortion induced by the FTOS, our proposed method relies on a layer-based approach. Experimental results show that our method is able to compute holograms of resolution (3840×2160) in real time at 24 frames per second, enabling its use in augmented reality applications.

Color digital hologram compression based on matching pursuit.

With the recent widespread interest for head-mounted displays applied to virtual or augmented reality, holography has been considered as an appealing technique for a revolutionary and natural 3D visualization system. However, due to the tremendous amount of data required by holograms and to the very different properties of holographic data compared to common imagery, compression of digital holograms is a highly challenging topic for researchers. In this study, we introduce a novel approach, to the best of our knowledge, for color hologram compression based on matching pursuit using an overcomplete Gabor's dictionary. A detailed framework, together with a GPU implementation, from hologram decomposition to bitstream generation, is studied, and the results are discussed and compared to existing hologram compression algorithms.

Hybrid approach for fast occlusion processing in computer-generated hologram calculation.

A hybrid approach for fast occlusion processing in computer-generated hologram calculation is studied in this paper. The proposed method is based on the combination of two commonly used approaches that complement one another: the point-source and wave-field approaches. By using these two approaches together, the proposed method thus takes advantage of both of them. In this method, the 3D scene is first sliced into several depth layers parallel to the hologram plane. Light scattered by the scene is then propagated and shielded from one layer to another using either a point-source or a wave-field approach according to a threshold criterion on the number of points within the layer. Finally, the hologram is obtained by computing the propagation of light from the nearest layer to the hologram plane. Experimental results reveal that the proposed method does not produce any visible artifact and outperforms both the point-source and wave-field approaches.