Autofocus Depth Estimation via Adaptive Blur Matching
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Solution Overview
Problem
Existing autofocus systems are slow and often fail to converge on a proper focus, especially in low-contrast or low-light conditions, and are prone to 'hunting' for the correct focal position, leading to inaccurate depth estimation.
Innovation Solution
The system performs multiple forms of depth estimation at each lens position, using a combination of techniques such as two-picture matching, Gaussian blur, pillbox blur, and wavelet transforms, and adapts blur matching with adaptive model fitting and slope correction to determine optimal blur matching, utilizing a weighted mean of estimation results for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single depth estimation method is used, then the system is simple and fast, but accuracy and reliability are insufficient especially in low-contrast or low-light conditions
Solution Approach 1:
The patent combines multiple depth estimation methods (two-picture matching, Gaussian blur, pillbox blur, wavelet transforms) into a unified system that processes images through all methods and selects the most reliable result, thereby improving accuracy without requiring a single complex method
Solution Approach 2:
The system dynamically adjusts which depth estimation method to use based on image characteristics such as contrast and lighting conditions, changing the processing parameters adaptively to optimize accuracy for different scenarios
2Speed
If traditional autofocus mechanisms are used, then the system is simple to implement, but focusing speed is slow and convergence is unreliable
Solution Approach 1:
The system uses depth estimation results as feedback to guide the autofocus process, continuously adjusting lens position based on measured depth and comparing it with expected depth values to achieve rapid and reliable convergence
Solution Approach 2:
The system performs preliminary depth estimation using multiple methods before final focus adjustment, preparing focus candidates in advance to enable faster convergence during actual focusing operation
3Measurement precision
If multiple depth estimation methods are applied, then accuracy and confidence in focus positioning improve, but processing time and computational complexity increase
Solution Approach 1:
The system applies different depth estimation methods selectively based on local image characteristics such as contrast and lighting conditions, using simpler methods where possible and reserving complex methods for challenging scenarios to optimize processing efficiency
Solution Approach 2:
The system performs partial depth estimation using multiple methods and selects only the necessary results for final focus determination, avoiding complete processing of all methods when simpler approaches suffice
Data Source
AI summary
Autofocusing is performed in response to a capturing object images upon which multiple depth estimation techniques are applied to yield a plurality of iterations. An iteration from one of these depth estimation techniques is selected based on results, such as based on largest absolute value, and checked. If the iteration fails the check, another of the iterations is selected and tested. Once a valid iteration is found, additional focus positions are executed in like manner from which an accurate focus position is determined.