Comprehensive deep learning model for 3D color holography

Abstract

Holography is a vital tool used in various applications from microscopy, solar energy, imaging, display to information encryption. Generation of a holographic image and reconstruction of object/hologram information from a holographic image using the current algorithms are time-consuming processes. Versatile, fast in the meantime, accurate methodologies are required to compute holograms performing color imaging at multiple observation planes and reconstruct object/sample information from a holographic image for widely accommodating optical holograms. Here, we focus on design of optical holograms for generation of holographic images at multiple observation planes and colors via a deep learning model, the CHoloNet. The CHoloNet produces optical holograms which show multitasking performance as multiplexing color holographic image planes by tuning holographic structures. Furthermore, our deep learning model retrieves an object/hologram information from an intensity holographic image without requiring phase and amplitude information from the intensity image. We show that reconstructed objects/holograms show excellent agreement with the ground-truth images. The CHoloNet does not need iteratively reconstruction of object/hologram information while conventional object/hologram recovery methods rely on multiple holographic images at various observation planes along with the iterative algorithms. We openly share the fast and efficient framework that we develop in order to contribute to the design and implementation of optical holograms, and we believe that the CHoloNet based object/hologram reconstruction and generation of holographic images will speed up wide-area implementation of optical holography in microscopy, data encryption, and communication technologies.

Wavelength-Independent Correlation Detection of Aberrations Based on a Single Spatial Light Modulator

“…Equation (5) shows that the wavelength λ can be moved outside the weighted sum. In this case, Equation (5) becomes independent of the wavelength of radiation.…”

“…With the development of devices, such as spatial light modulators (SLM) based on liquid crystals (LC-SLM) [3] and micromirrors (DMD), multichannel optical pattern recognition systems also began to develop. These systems began to use the color component of the image as an information parameter [4,5]. Such devices allow solving pattern recognition problems by spatial and spectral parameters in real time [6].…”

HoloHDR: Multi-color Holograms improve Dynamic Range

“…Fourier Rainbow holograms with incoherent light sources [25,26] help map the same image to a different perspective (directions) in the Fourier plane. Yolalmaz and Yüce [27] introduce a deep-learning model that could generate holograms at various depths using various separate colors. Previous works did not involve improving dynamic range by optimizing multi-color holograms and their light dosages.…”

Fast Hologram Calculation Method Based on Wavefront Precise Diffraction

“…For different representations of 3D objects, there are different acceleration algorithms, such as point-based methods, polygon-based methods, and layer-based methods to calculate the hologram of 3D objects [ 4 , 5 , 6 , 7 , 8 , 9 ]. Moreover, the calculation of the hologram can be accelerated by using large-scale integrated circuits and deep learning [ 10 , 11 , 12 ]. However, holographic 3D display technology still has some technical difficulties and challenges, such as the narrow field of view [ 13 ], serious speckle noise [ 14 ], and slow calculation speed [ 15 ].…”