Color Interpolation Using Edge Gradient Analysis
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Solution Overview
Problem
Existing color interpolation methods for image sensors often introduce false colors and blur around edges due to neglecting edge patterns, and they either incorrectly detect edges or increase hardware complexity and cost.
Innovation Solution
A color interpolation processing method that generates a two-dimensional matrix from captured scenes, calculates column and row color difference components, identifies a minimum color difference variation matrix, and uses this to accurately determine missing color components while reducing hardware complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional color interpolation methods are used to calculate missing color components, then the interpolation process is simple, but false colors and blur are introduced around edges
Solution Approach 1:
The patent applies local quality by differentiating interpolation strategies based on local edge characteristics. Edge detection is performed to identify regions with edge patterns, and different interpolation formulas are applied: one formula is used for pixels located at edges, while another formula is used for pixels in non-edge regions. This localized approach ensures high color accuracy around edges while maintaining computational efficiency in smooth regions.
Solution Approach 2:
The patent changes the parameter of interpolation formula selection based on edge detection results. By detecting edge patterns and calculating gradient magnitudes, the system dynamically selects appropriate interpolation parameters (different formulas) for different spatial locations, thereby adapting the interpolation process to local image characteristics and avoiding false colors and blur around edges.
2Manufacturing precision
If edge detection methods are used to improve color interpolation accuracy, then color accuracy around edges is improved, but incorrect edge detection occurs leading to wrong values
Solution Approach 1:
The patent introduces an intermediary gradient magnitude calculation as a mediator between edge detection and interpolation. Instead of directly using binary edge detection results, the system calculates gradient magnitudes in horizontal and vertical directions, compares them to determine the dominant edge direction, and then selects the appropriate interpolation formula. This intermediary step provides more reliable and nuanced edge information, reducing incorrect edge detection.
3Manufacturing precision
If weighted methods are used to calculate color components, then interpolation accuracy is improved, but hardware cost and complexity increase
Solution Approach 1:
The patent segments the interpolation process into distinct stages: edge detection, gradient calculation, formula selection, and final computation. By dividing the process into modular segments, the system achieves high accuracy through multiple calculation steps while maintaining hardware efficiency through systematic organization. Each segment performs a specific function, avoiding the need for complex weighted methods while achieving comparable or superior accuracy.
Data Source
AI summary
A scene is captured. A two-dimensional matrix comprising a plurality of image detection components is generated according to the captured scene. Each image detection component corresponds to one color component. A plurality of matrixes are generated according to the image detection components. One image detection component and a portion of matrixes are calculated to obtain the missing color components.