US Patent Application 18308447. REAL TIME HOLOGRAPHY USING LEARNED ERROR FEEDBACK simplified abstract
Contents
REAL TIME HOLOGRAPHY USING LEARNED ERROR FEEDBACK
Organization Name
Inventor(s)
Alexey Supikov of Santa Clara CA (US)
Qiong Huang of San Jose CA (US)
Ronald T. Azuma of San Jose CA (US)
REAL TIME HOLOGRAPHY USING LEARNED ERROR FEEDBACK - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 18308447 Titled 'REAL TIME HOLOGRAPHY USING LEARNED ERROR FEEDBACK'
Simplified Explanation
The abstract discusses techniques for creating holographic images for a heads-up display. These techniques involve using a machine learning model to analyze the target image and generate data. This data is then used to determine a phase pattern through an iterative propagation feedback model. The model calculates a feedback strength value, which is used to generate a phase diffraction pattern for displaying on the holographic plane of the heads-up display.
Original Abstract Submitted
Techniques related to generating holographic images for a holographic heads up display are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane of the heads up display.
