Cross-Color Image Sharpness via Weighted Color Contributions
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Solution Overview
Problem
Existing cross-color image processing methods are complex and inaccurate in determining the sharpest individual color images (R, G, or B) for objects at different distances, leading to difficulties in achieving sharp composite color images due to chromatic aberration in imaging lenses.
Innovation Solution
A method that calculates weight factor coefficients based on detected color intensity values within a pixel window to form weighted color contributions, allowing for the creation of a processed composite color image without determining the sharpest individual color image, thereby improving the sharpness of all color images and reducing computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing cross-color image processing methods are used to correct less sharp images using information from the sharpest image, then image sharpness can be restored, but the computational process becomes long and complex
Solution Approach 1:
The patent changes the approach from identifying the sharpest image first to directly calculating weight factor coefficients that represent sharpness characteristics. By computing weights based on color intensity values and their variations across multiple color channels simultaneously, the method avoids the complex multi-step process of identifying sharpest images and performing separate restoration operations.
Solution Approach 2:
The patent merges the sharpness enhancement process across all color channels by calculating weight factor coefficients that incorporate information from multiple colors (R, G, B) simultaneously. This unified approach allows the system to enhance sharpness for all colors in a single integrated computational process rather than treating each color channel separately.
2Device complexity
If a single sharpest individual color image is defined for the entire image, then cross-color processing can be performed, but accuracy decreases for objects at different distances
Solution Approach 1:
The patent applies local quality by calculating weight factor coefficients for different regions of the image based on local color intensity variations. The weight factors are computed using a sliding window approach that considers neighboring pixels, allowing the sharpness enhancement to adapt to local characteristics of different objects at different distances within the same image.
Solution Approach 2:
The patent introduces dynamics by making the weight factor coefficients adaptive rather than fixed. The weights are dynamically calculated based on the actual color intensity values and their variations in each local region, allowing the system to automatically adjust to different object distances and sharpness characteristics without requiring manual selection or complex classification.
3Manufacturing precision
If the image sensor position is adjusted to optimize one color image (e.g., R), then that color image becomes sharp, but other color images (G and B) become less sharp
Solution Approach 1:
The patent introduces weight factor coefficients as intermediaries that mediate between the detected color images and the final processed images. These weight factors incorporate sharpness information from multiple color channels and use it to enhance all colors simultaneously, acting as a bridge that transfers sharpness characteristics across color channels without requiring physical sensor adjustment.
Solution Approach 2:
The patent makes the sharpness enhancement process universal by applying the same weight factor coefficient calculation and application process to all color channels (R, G, B). The method enhances sharpness for multiple colors simultaneously using a unified approach, making the system adaptable to different color imaging scenarios without requiring separate processing for each color.
Data Source
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AI summary
Systems and methods for processing a detected composite color image to form a processed composite color image includes the following, for each of a plurality of pixels in the image: (1) identifying a window of pixels in the image that surrounds the pixel, (2) calculating a weight factor coefficient for each detected color from detected color intensity values of the pixels that surround the pixel, (3) calculating raw color contributions corresponding to each nonselected color, (4) multiplying each of the detected color values of a selected color and the raw color contributions corresponding to the nonselected colors, with corresponding weight factor coefficients, to form weighted color contributions, and (5) summing the weighted color contributions to form a processed color intensity value for the pixel.