![]() These algorithms are easy to employ and yield good performance but require iterative optimization. Considering wide-area implementation of optical holography for both retrieving object/hologram information and generation of a holographic image, a versatile methodology with a short design/optimization duration is highly demanded.įor generation of optical holographic images and object/hologram recovery, there are a variety of algorithms frequently used 19, 20, 21, 22, 23, 24. For generation of a holographic image, fine-tuned phase/amplitude distribution of a hologram is required that increases design duration of a hologram. Optical holography enables image formation at different observation planes or through a sample without requiring any focusing elements or mechanical scanning. ![]() Besides the information gained from intensity images is reversible, and the phase and amplitude information can be used to generate intensity images for numerous applications such as imaging 9, 10, 11, photostimulation 12, printing 13, optical beam steering 14, aberration correction 15, display 16, 17, and augmented reality 18. Reconstructed object/hologram from an intensity image has important roles in high-security encryption 1, 2, microscopy 3, data storage 4, 3D object recognition 5, and planar solar concentrator 6, 7, 8. Optical holography is a superior tool to retrieve phase and amplitude of light from an intensity image, which bears detailed information of the object as size, shape, and refractive index. 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. 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 show that reconstructed objects/holograms show excellent agreement with the ground-truth images. 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. The CHoloNet produces optical holograms which show multitasking performance as multiplexing color holographic image planes by tuning holographic structures. ![]() 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. 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. Generation of a holographic image and reconstruction of object/hologram information from a holographic image using the current algorithms are time-consuming processes. Holography is a vital tool used in various applications from microscopy, solar energy, imaging, display to information encryption. ![]()
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