![]() Yoshiyuki Ishii and Tomoyoshi Ito of the Graduate School of Engineering, Chiba University were also a part of this study, published in Optics and Lasers in Engineering. The Chiba researchers led by Professor Tomoyoshi Shimobaba of the Graduate School of Engineering, propose a novel approach based on deep learning that further streamlines hologram generation by producing 3D images directly from regular 2D color images captured using ordinary cameras. This approach circumvents many computational challenges associated with the conventional method and represents an easier approach for generating holograms. These can create holograms directly from the 3D data captured using RGB-D cameras that capture both color and depth information of an object. This makes the generation of holograms challenging and limits their widespread use.ĭeep-learning methods have also been proposed for generating holograms. However, this technique is computationally highly intensive as it requires the use of a special camera to capture the 3D images. ![]() Holograms, which offer enormous potential for medical imaging, manufacturing, and virtual reality, are traditionally constructed by recording the 3Ddata of an object and the interactions of light with the object. Holograms that offer a 3D view of objects provide a level of detail that is unattainable by regular 2D images. This approach can simplify 3D hologram generation and can find applications in numerous fields, including healthcare and entertainment. Leveraging recent developments in deep learning, researchers from Chiba University, Japan, have now developed what they describe as a “game-changing” approach that utilizes neural networks to transform 2D color images into 3D holograms. Chiba approach uses neural networks to transform 2D images into 3D holograms.Holograms have long held the promise of offering immersive 3D experiences, but the challenges involved in generating them have limited their widespread use. ![]()
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