Focus Detection Algorithm Combining Chromatic and Wavelet Features
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Solution Overview
Problem
Existing focus detection methods in digital imaging rely solely on a single feature, which lacks reliability in distinguishing between in-focus and slightly out-of-focus images, leading to suboptimal camera autofocus performance.
Innovation Solution
A focus detection algorithm that combines multiple features, including iterative blur estimation, wavelet energy ratio, chromatic aberration, and Chebyshev moment ratios, to evaluate sharpness and determine image focus, using a multivariate Gaussian distribution and coarse-to-fine depth map construction to enhance reliability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only one feature is used for focus detection, then the detection method is simple, but the reliability to distinguish in-focus and slightly out-of-focus images is insufficient
Solution Approach 1:
The patent combines multiple features (chromatic aberration, wavelet energy ratio, Chebyshev moment ratio, and iterative blur estimation) into a unified focus detection framework. This merging of multiple detection features enables reliable distinction between in-focus and slightly out-of-focus images, resolving the contradiction between reliability and complexity by integrating several simple features into a comprehensive detection system.
2Measurement precision
If multiple features are combined for focus detection, then the reliability and accuracy are improved, but the algorithm complexity increases
Solution Approach 1:
The patent segments the focus detection process into distinct computational stages: chromatic aberration calculation, wavelet transform for energy ratio computation, Chebyshev moment calculation, and iterative blur estimation. Each feature is computed independently and then integrated, allowing precise focus detection through modular processing that manages algorithmic complexity through structured segmentation.
Solution Approach 2:
The patent transforms the focus detection problem from a single-dimensional assessment to a multi-dimensional analysis by incorporating features from different domains: optical physics (chromatic aberration), signal processing (wavelet energy), mathematical moments (Chebyshev moments), and iterative estimation (blur estimation). This dimensional expansion enables high-precision focus detection while organizing complexity across multiple analytical dimensions.
3Loss of time
If a single image is used for focus detection, then the processing time is reduced, but the ability to determine focus accurately is limited
Solution Approach 1:
The patent performs preliminary computational preparations by pre-calculating and storing reference data for Chebyshev moment ratios and wavelet energy ratios across different focus conditions. During actual focus detection, these pre-computed references are quickly compared against the input image features, enabling rapid single-image focus determination with high accuracy without requiring multiple images or iterative temporal processing.
Data Source
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AI summary
Focus detection is to determine whether an image is in focus or not. Focus detection is able to be used for improving camera autofocus performance. Focus detection by using only one feature does not provide enough reliability to distinguish in-focus and slightly out-of-focus images. A focus detection algorithm of combining multiple features used to evaluate sharpness is described herein. A large image data set with in-focus and out-of-focus images is used to develop the focus detector for separating the in-focus images from out-of-focus images. Many features such as iterative blur estimation, FFT linearity, edge percentage, wavelet energy ratio, improved wavelet energy ratio, Chebyshev moment ratio and chromatic aberration features are able to be used to evaluate sharpness and determine big blur images.