18045696. MACHINE LEARNING MODEL TRAINING USING SYNTHETIC DATA FOR UNDER-DISPLAY CAMERA (UDC) IMAGE RESTORATION simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
- 1 MACHINE LEARNING MODEL TRAINING USING SYNTHETIC DATA FOR UNDER-DISPLAY CAMERA (UDC) IMAGE RESTORATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING MODEL TRAINING USING SYNTHETIC DATA FOR UNDER-DISPLAY CAMERA (UDC) IMAGE RESTORATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MACHINE LEARNING MODEL TRAINING USING SYNTHETIC DATA FOR UNDER-DISPLAY CAMERA (UDC) IMAGE RESTORATION
Organization Name
Inventor(s)
Hamid R. Sheikh of Allen TX (US)
John Seokjun Lee of Allen TX (US)
MACHINE LEARNING MODEL TRAINING USING SYNTHETIC DATA FOR UNDER-DISPLAY CAMERA (UDC) IMAGE RESTORATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18045696 titled 'MACHINE LEARNING MODEL TRAINING USING SYNTHETIC DATA FOR UNDER-DISPLAY CAMERA (UDC) IMAGE RESTORATION
Simplified Explanation
The method described in the patent application involves using a spatially-variant point spread function associated with an under-display camera to generate a synthetic sensor image for training a machine learning model.
- The method involves identifying a spatially-variant point spread function associated with an under-display camera.
- The method includes generating a ground truth image and performing a convolution based on the point spread function to generate a synthetic sensor image.
- The synthetic sensor image and ground truth image are provided as an image pair to train a machine learning model for under-display camera point spread function inversion.
Potential Applications
This technology could be applied in the development of advanced under-display camera systems for smartphones, tablets, and other electronic devices.
Problems Solved
This technology addresses the challenge of accurately simulating images captured by under-display cameras for training machine learning models.
Benefits
The use of synthetic sensor images generated based on spatially-variant point spread functions can improve the performance and accuracy of machine learning models for under-display camera systems.
Potential Commercial Applications
The technology could be utilized by companies developing under-display camera technology for consumer electronics, leading to improved image quality and performance.
Possible Prior Art
One possible prior art in this field could be research or patents related to image simulation techniques for training machine learning models in the context of camera systems.
Unanswered Questions
How does this technology compare to existing methods for training machine learning models for under-display cameras?
This article does not provide a direct comparison with existing methods or technologies in the field.
What are the potential limitations or challenges of implementing this technology in real-world under-display camera systems?
The article does not address the potential limitations or challenges that may arise when implementing this technology in practical under-display camera systems.
Original Abstract Submitted
A method includes identifying, using at least one processing device of an electronic device, a spatially-variant point spread function associated with an under-display camera. The spatially-variant point spread function is based on an optical transmission model and a layout of a display associated with the under-display camera. The method also includes generating, using the at least one processing device, a ground truth image. The method further includes performing, using the at least one processing device, a convolution of the ground truth image based on the spatially-variant point spread function in order to generate a synthetic sensor image. The synthetic sensor image represents a simulated image captured by the under-display camera. In addition, the method includes providing, using the at least one processing device, the synthetic sensor image and the ground truth image as an image pair to train a machine learning model to perform under-display camera point spread function inversion.
