Adaptive Autofocus Lens Positioning Using Historical Data
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Solution Overview
Problem
Contemporary passive autofocus mechanisms in cameras require testing a substantial number of focusing lens positions, leading to longer autofocus times and a risk of sub-optimal focus due to local sharpness maxima not being the highest.
Innovation Solution
The system uses historic and current information, such as sharpness scores, aperture settings, shutter speed, and zoom positions, to predict the next focusing lens position to test, reducing the number of positions that need to be searched for best focus, and employing lookup tables and adaptive step sizes for more efficient focusing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a substantial number of focusing lens positions are tested to determine the best focus, then the reliability of achieving optimal focus is improved, but the time required for autofocus increases
Solution Approach 1:
The system performs preliminary actions by using active autofocus mechanisms to estimate subject distance and predict the likely best focus position before conducting the passive autofocus search. This preliminary estimation allows the system to start the search from a more informed position, reducing the number of lens positions that need to be tested while maintaining focus accuracy.
Solution Approach 2:
The system implements feedback by continuously monitoring sharpness scores at each tested lens position and using this information to adaptively adjust the search strategy. The feedback mechanism allows the system to identify local maxima and continue searching toward the global maximum, ensuring reliable focus acquisition while optimizing the search path to reduce time.
2Loss of time
If local searching is used to reduce the number of focusing lens positions tested, then the autofocus time is reduced, but the risk of settling at a sub-optimal local sharpness maximum increases
Solution Approach 1:
The system applies dynamics by making the search strategy adaptive rather than static. The search parameters, including step size and search range, are dynamically adjusted based on feedback from sharpness measurements. This allows the system to perform local searching efficiently while maintaining the capability to escape local maxima and find the global maximum, thus balancing speed and reliability.
Solution Approach 2:
The system changes parameters during the autofocus process by adjusting the search range and step size based on the current search state and sharpness feedback. When a local maximum is detected, the system modifies search parameters to continue exploring other regions, ensuring that the final focus position is the global maximum rather than a sub-optimal local maximum.
3Reliability
If a fixed searching sequence with many pre-determined focusing lens locations is used, then the likelihood of finding the best focus is improved, but the autofocus process becomes slower
Solution Approach 1:
The system performs preliminary distance estimation using active autofocus mechanisms before executing the passive autofocus search. This preliminary action provides advance information about the subject distance, allowing the system to prioritize testing lens positions that are more likely to contain the best focus, thereby reducing the number of positions that need to be tested while maintaining high accuracy.
Solution Approach 2:
The system transforms the fixed searching sequence into a dynamic, adaptive search process. The search sequence is adjusted in real-time based on feedback from sharpness measurements and the predicted subject distance. This dynamic approach allows the system to focus computational resources on the most promising lens positions, improving both speed and accuracy compared to a rigid fixed sequence.
Data Source
AI summary
A method and system for effecting adaptive autofocusing, such as for a camera, are disclosed. The method can comprise obtaining focusing information, such as historic focusing actuator current or voltage information or focusing lens position information, storing the focusing information, and subsequently using the focusing information to facilitate a determination of a best position of a focusing lens during a focusing process. The use of such focusing information can result in a better choice for the next focusing lens position to be tested, such that the focusing process can be performed more rapidly. The use of such focusing information can also mitigate the undesirable effects of production variations and/or component wear.