Auto-focus Method Using Blur Level Relational Expressions
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Solution Overview
Problem
Conventional auto-focus methods in image-capturing devices require switching between normal and macro modes and are slow due to the need for multiple images to determine the optimum focal point, making them unsuitable for high-speed photography.
Innovation Solution
An auto-focus method that calculates the focus lens position by obtaining images at two or three fixed positions and using blur level relational expressions to determine the optimal focus without requiring mode switching, allowing for rapid adjustment of focus based on image blur levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional auto-focus methods use multiple images to determine the optimum focal point, then the precision of focus is improved, but the shutter lag increases and photography speed decreases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing blur level relational expressions for multiple object distances before actual photography. During shooting, the system only needs to evaluate these pre-prepared expressions with current blur levels, rather than performing complex optimization calculations in real-time. This allows rapid focus determination using fewer images while maintaining high precision.
Solution Approach 2:
The patent creates a simplified model (blur level relational expressions) that copies the essential relationship between focus lens position and image blur for different object distances. Instead of using the full complex optical system for focus determination, the invention uses these pre-derived mathematical expressions that replicate the focus behavior, enabling fast evaluation with minimal computation.
2Adaptability or versatility
If conventional auto-focus methods require switching between macro mode and normal mode, then the adaptability to different object distances is improved, but the device complexity and operation complexity increase
Solution Approach 1:
The patent applies universality by creating a unified auto-focus method that works for both macro and normal photography modes without requiring separate mode settings. The blur level relational expressions are designed to cover a wide range of object distances, allowing the same computational approach to determine optimal focus for close-up and distant subjects alike, eliminating the need for mode switching.
Solution Approach 2:
The patent uses parameter changes by varying the object distance parameter in the blur level relational expressions to adapt to different photography scenarios. Instead of changing operational modes, the system adjusts which pre-calculated expressions are used based on the estimated object distance, enabling seamless transition between macro and normal photography through parameter selection rather than mode switching.
3Measurement precision
If conventional auto-focus methods use high pass filtering and hill climbing, then the precision of focus is improved, but the number of images required increases and processing time increases
Solution Approach 1:
The patent replaces the mechanical iterative optimization process (hill climbing) with direct mathematical evaluation using pre-derived blur level relational expressions. Instead of sequentially adjusting focus lens position and capturing multiple images to climb toward the optimum, the invention substitutes this mechanical search with direct calculation using stored expressions, achieving precise focus determination with fewer images and faster processing.
Solution Approach 2:
The patent applies preliminary action by pre-calculating the blur level relational expressions for various object distances before actual photography sessions. This preliminary preparation stores the essential focus characteristics in mathematical form, so during shooting, the system only needs to evaluate these pre-computed expressions rather than performing complex filtering and optimization algorithms in real-time, significantly improving photography speed.
Data Source
AI summary
An auto-focus method, medium, and apparatus for image-capturing. The auto-focus method includes obtaining a first image by placing a focus lens of a corresponding image-capturing apparatus at a first fixed position, obtaining a second image by placing the focus lens at a second fixed position; calculating blur levels of the first and second images, and determining a position of the focus lens by substituting the blur levels of the first and second images into each of a plurality of blur level relational expressions, which are derived from a plurality of pairs of images of respective corresponding objects at different distances from an image sensor module, each of the pairs of images being obtained by placing the focus lens at the first and second fixed positions, respectively.