Self-Calibrating Autofocus Using Unsupervised Learning
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Solution Overview
Problem
Conventional autofocus calibration in digital cameras is tedious, labor-intensive, and costly, often resulting in inaccurate calibration data that can compromise focusing speed and accuracy, especially when factory calibration is not performed or is inaccurate.
Innovation Solution
A self-calibration autofocus process that uses unsupervised learning to iteratively estimate focus range parameters from measurement data during autofocus iterations, updating calibration data in memory for improved lens positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional factory calibration is performed, then lens positioning accuracy is improved, but manufacturing cost and time increase
Solution Approach 1:
The camera device performs self-calibration of the autofocus system using unsupervised learning algorithms. The device automatically captures images at multiple lens positions, analyzes focus metrics, and determines calibration parameters without requiring external calibration equipment or manual intervention, thereby eliminating factory calibration costs while maintaining positioning accuracy
Solution Approach 2:
The system changes the operational parameters by performing calibration during normal device operation rather than during manufacturing. The calibration process dynamically adjusts lens position parameters based on real-world image data, transforming the calibration from a static factory procedure to a dynamic post-manufacturing process
2Productivity
If factory calibration is not performed, then manufacturing cost decreases, but autofocus performance and accuracy deteriorate
Solution Approach 1:
The system performs preliminary calibration actions automatically during the first use or initial operation of the device. By pre-calibrating the autofocus system through unsupervised learning before normal operation begins, the device ensures reliable autofocus performance without requiring costly factory calibration procedures
Solution Approach 2:
The self-calibration process uses feedback from captured images and focus metrics to iteratively improve lens positioning accuracy. The system continuously monitors autofocus performance and adjusts calibration parameters based on measured focus quality, ensuring reliable operation even without initial factory calibration
3Manufacturing precision
If traditional calibration algorithms are used, then calibration can be performed, but the process is tedious and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical calibration procedures with an automated computational system. Instead of physically adjusting lens positions and using external measurement equipment, the system uses software-based unsupervised learning algorithms that automatically determine calibration parameters from image data, eliminating labor-intensive operations while maintaining precision
Solution Approach 2:
The calibration system serves itself by automatically capturing necessary images, analyzing focus metrics, computing calibration parameters, and storing results without human intervention. This self-calibration capability transforms the calibration process from a manual task requiring skilled operators to an automated procedure that executes independently
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 reduces the need for costly factory calibration, enhances autofocus performance by adapting to device-specific variations, and ensures accurate focusing even without initial calibration data, leading to faster and more reliable autofocus functionality.
Implementation Method 1
The movement of the lens is controlled by a camera actuator or motor that converts electric current, in case of a voice coil motor (VCM) actuator, into motion.
Data Source
AI summary
A systems, article, and method to provide automatic focus with self-calibration.