Autofocus Method Using Integral Projection Vector Convolution
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Solution Overview
Problem
Autofocus systems, especially passive systems, face challenges in low-light conditions and with subjects of low contrast, leading to focus failures and reduced accuracy.
Innovation Solution
The method involves acquiring multiple images focused at different distances, computing integral projection vectors, and convolving them with filters to determine the sharpest image and estimate blur width, allowing for precise focus adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If passive autofocus systems are used in low-light conditions or with low-contrast subjects, then the system can operate without additional lighting, but focus accuracy deteriorates and focus failures increase
Solution Approach 1:
The patent introduces an autofocus assist beam (infrared or visible light) as an intermediary element to illuminate the subject when passive autofocus fails in low-light or low-contrast conditions. This assist beam provides the necessary illumination for the passive AF system to function accurately without requiring the camera to switch to active AF modes
Solution Approach 2:
The system changes the illumination parameters by introducing an infrared or visible assist beam with specific wavelength and intensity characteristics. This parameter change enables the passive autofocus system to detect sufficient contrast and achieve accurate focus in conditions where normal visible light is insufficient
2Measurement precision
If multiple images are acquired and processed with integral projection vectors and convolution filters, then focus accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary actions by acquiring multiple images at different focus distances and computing integral projection vectors before the actual focus determination. This preliminary processing organizes the data in advance, enabling faster and more accurate focus calculation through subsequent convolution operations with filters
Solution Approach 2:
The autofocus process is segmented into distinct stages: image acquisition at different focus distances, integral projection vector computation, convolution with filters of different lengths, and focus determination. This segmentation allows each stage to be optimized independently and facilitates parallel processing to reduce overall computation time
Data Source
AI summary
An autofocus method includes acquiring multiple images each having a camera lens focused at a different focus distance. A sharpest image is determined among the multiple images. Horizontal, vertical and/or diagonal integral projection (IP) vectors are computed for each of the multiple images. One or more IP vectors of the sharpest image is/are convoluted with multiple filters of different lengths to generate one or more filtered IP vectors for the sharpest image. Differences are computed between the one or more filtered IP vectors of the sharpest image and one or more IP vectors of at least one of the other images of the multiple images. At least one blur width is estimated between the sharpest image and the at least one of the other images of the multiple images as a minimum value among the computed differences over a selected range. The steps are repeated one or more times to obtain a sequence of estimated blur width values. A focus position is adjusted based on the sequence of estimated blur width values.