Logistic Function Color Fringe Correction
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Solution Overview
Problem
Color fringes caused by chromatic aberration and blooming effects in image sensors lead to significant color distortion, especially near object boundaries, which worsen with increased image resolution, and existing methods fail to correct these distortions without altering the original image colors.
Innovation Solution
A method that detects transition regions in images, models color difference distributions using a logistic function, and corrects color values within these regions to maximize correlation, thereby reducing color fringes without corrupting the original image colors, while optimizing the logistic function with reduced parameters to minimize calculation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image resolution is increased for more detailed image acquisition, then image detail quality is improved, but color fringe distortion becomes more serious
Solution Approach 1:
The patent divides the image into transition regions (where color fringes occur) and non-transition regions (where original colors should be preserved). By segmenting the image data and applying different processing strategies to each region, the method corrects color fringes in transition regions while maintaining original colors in non-transition regions, thus resolving the contradiction between image detail quality and color fringe distortion.
Solution Approach 2:
The patent applies different color correction strategies to different regions of the image. In transition regions, color fringe correction is applied using logistic function modeling; in non-transition regions, original colors are preserved. This local differentiation allows the system to improve color accuracy where needed without compromising the natural appearance of the image, resolving the contradiction between detail quality and color distortion.
2Object-affected harmful factors
If conventional color fringe correction methods are applied, then color distortion is reduced, but original image colors are corrupted
Solution Approach 1:
The patent segments the image into transition regions and non-transition regions, applying color fringe correction only to the former while preserving original colors in the latter. This segmentation-based approach ensures that color distortion is reduced without corrupting original image colors, as the correction is locally applied only where chromatic aberration occurs.
Solution Approach 2:
The patent uses correlation calculation as feedback to evaluate the quality of color correction. By calculating correlation between corrected and original color values, the system can adjust the correction parameters to maximize correlation, ensuring that color fringes are corrected while original colors are preserved. This feedback mechanism prevents color corruption by continuously optimizing the correction process.
3Measurement precision
If complex optimization parameters are used for the logistic function, then color fringe correction accuracy is improved, but calculation cost increases
Solution Approach 1:
The patent changes the parameters of the logistic function to optimize color fringe correction accuracy. By adjusting parameters such as the logistic function coefficients and correlation thresholds, the system achieves accurate color fringe correction while controlling calculation complexity. This parameter optimization balances correction accuracy with computational efficiency, resolving the contradiction between precision and energy consumption.
Data Source
AI summary
A color fringe is corrected by detecting a transition region that includes pixels adjacent in a linear direction. A color difference distribution in the transition region is modeled by a logistic function. Pixel color values in the transition region are corrected using the logistic function to maximize a correlation between a correction color and a reference color with respect to the transition region. Color distortion such as color fringes is corrected without corrupting the original colors of the image by modeling the color difference by the logistic function while maximizing the correlation using information of the undistorted region. A calculation cost is reduced by reducing the number of the parameters required to optimize the logistic function.