Holographic Content

Holographic content generation focuses on creating realistic three-dimensional images using computational methods, primarily aiming to improve image quality, reduce computational cost, and expand applications. Current research emphasizes the use of deep learning models, such as neural networks (including quantized versions for efficiency) and generative adversarial networks (GANs), often integrated with established techniques like the angular spectrum method or the Gerchberg-Saxton algorithm. These advancements are driving progress in diverse fields, including augmented reality displays, microscopic imaging, and identity document verification, by enabling faster, more efficient, and higher-quality hologram generation from various input data types (e.g., RGB images, depth maps, or light fields).