Magnification Chromatic Aberration Correction in Bayer Array Imaging
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Solution Overview
Problem
Conventional methods for correcting magnification chromatic aberration in wide-angle imaging systems require large memory capacity and expensive RAM, making them costly and inefficient, especially when dealing with large chromatic aberrations across different color components.
Innovation Solution
A method and apparatus for processing image data that corrects magnification chromatic aberration by performing coordinate transformation on each pixel in the image data, using a magnification-chromatic-aberration correcting unit, and compensating for defective pixels due to the color filter array, allowing for reduced memory requirements and maintaining the Bayer array before and after transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If magnification chromatic aberration is corrected by performing coordinate transformation for each color component (R, G, B) separately, then the chromatic aberration correction accuracy is improved, but the memory capacity requirement increases significantly and device cost increases
Solution Approach 1:
The patent merges the correction process for all color components into a single coordinate transformation operation applied to the Bayer array data before color separation. Instead of transforming R, G, and B signals independently with separate memory buffers, the invention transforms the raw Bayer pattern data once, then performs color interpolation after transformation. This combining of operations reduces memory capacity requirements while maintaining correction accuracy.
Solution Approach 2:
The patent performs the coordinate transformation on the Bayer array data before color separation and interpolation. By applying the transformation preliminary to the color processing steps, the invention avoids the need for separate transformation operations on each color component. This preliminary action allows subsequent color interpolation to be performed on already-transformed data, reducing overall memory requirements.
2Measurement precision
If separate memory is used for each color component to enable independent addressing, then the chromatic aberration correction accuracy is improved, but the device cost increases due to requiring expensive 3-port RAM or time-sharing driving
Solution Approach 1:
The patent combines the processing of all color components into a single coordinate transformation operation on Bayer array data. By merging the transformation operations for R, G, and B signals into one unified process applied before color separation, the invention eliminates the need for multiple independent memory buffers and complex multi-port addressing mechanisms, thereby reducing device complexity.
Solution Approach 2:
The patent uses the Bayer array pattern as a template that maintains its structural relationships through coordinate transformation. By transforming the Bayer pattern coordinates rather than separate color signals, the invention preserves the color filter array structure information, allowing subsequent color interpolation to proceed with simpler memory access patterns and reduced addressing complexity.
3Ease of operation
If color interpolation is performed before magnification chromatic aberration correction, then the processing flow is simplified, but the correction accuracy deteriorates because the Bayer array structure is lost
Solution Approach 1:
The patent performs coordinate transformation on the Bayer array data before color interpolation. By applying the magnification chromatic aberration correction preliminary to the color separation and interpolation steps, the invention maintains the Bayer array structural information throughout the transformation process. This preliminary action ensures that the color filter array pattern relationships are preserved, enabling accurate subsequent color interpolation while maintaining correction precision.
Solution Approach 2:
The patent segments the image processing into distinct stages: first coordinate transformation on Bayer array data, then color interpolation. This segmentation allows the transformation to operate on the structured Bayer pattern while preserving its relationships, and enables the interpolation stage to work with already-transformed data. The clear separation of these operations maintains both processing efficiency and correction accuracy.
Data Source
AI summary
A magnification chromatic aberration included in image data in a color filter array captured by an imaging device is corrected by performing a coordinate transformation with respect to each pixel in the image data. A defective pixel due to the color filter array is compensated with respect to magnification-chromatic-aberration-corrected image data.