Deep Learning Hologram Generation Reducing Computational Burden
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Solution Overview
Problem
Current holographic display technology faces challenges in processing and displaying high-data-volume 360-degree image content in real time, leading to high computational burdens and passive user experiences, with a need for interactive holographic content generation and display.
Innovation Solution
A method and apparatus utilizing deep learning engines to generate and process hologram data, including depth map generation and complex value hologram calculation using FFT-based CGH algorithms or deep learning engines, enabling fast and interactive holographic content creation and display in a 3D space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional holographic image processing methods are used, then image quality is maintained, but processing speed is slow and computational burden is high
Solution Approach 1:
The patent replaces traditional mechanical/computational hologram generation methods with a deep learning-based system. A neural network model is trained to directly generate hologram images from input images, substituting the complex mathematical computations (FFT, phase extraction, amplitude modulation) with learned patterns from training data, thereby significantly reducing processing time and computational burden.
Solution Approach 2:
The patent applies preliminary action by pre-training the deep learning model with大量 training data consisting of input images and their corresponding hologram images. This pre-training phase allows the model to learn the complex mapping relationships in advance, so that during actual application, the model can directly generate high-quality holograms without requiring real-time complex calculations.
2Adaptability or versatility
If high-data-volume 360-degree image content is processed, then complete holographic coverage is achieved, but real-time processing becomes difficult
Solution Approach 1:
The patent replaces traditional real-time holographic processing systems with a deep learning-based generation system. The neural network, after being trained on comprehensive 360-degree holographic data, can rapidly generate hologram images for any viewing angle without requiring real-time computational reconstruction, thus achieving both complete coverage and real-time performance.
3Manufacturing precision
If traditional hologram reconstruction methods are used, then accurate 3D reconstruction is achieved, but user interaction capability is limited
Solution Approach 1:
The patent applies universality by designing a holographic display system that can perform multiple functions: it can reconstruct accurate 3D holographic images while also supporting various user interactions such as gesture recognition, voice commands, and touch interfaces. The deep learning model generates holograms that respond to user inputs, making the system both precise and interactive.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions (gestures, voice, touch) are detected and processed, and the deep learning model generates corresponding holographic responses in real-time. This feedback loop enables natural user interaction while maintaining high reconstruction accuracy through the trained neural network.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for high-speed generation and display of holographic content, reducing computational burdens and enabling interactive user experiences, thus overcoming the limitations of existing technologies in real-time processing and reconstruction of holographic images.
Implementation Method 1
a spatial light modulator configured to form a diffraction pattern on the basis of the received hologram data and to modulate light incident on the formed diffraction pattern
Implementation Method 2
an optical module configured to irradiate coherent light onto the diffraction pattern so that the hologram image is reconstructed in a 3D space on the basis of light modulated by the spatial light modulator
Data Source
AI summary
A method of generating hologram data in a holographic display apparatus, including a hologram processing device generating hologram data and a display terminal reconstructing a hologram image in a three-dimensional (3D) space on the basis of the generated hologram data, includes generating depth map image data from input image data by using a deep learning engine for generating a depth map and calculating a complex value hologram on the basis of the depth map image data and the input image data to generate the hologram data.