Bayer Matrix Color Interpolation via Segmented Luminance Chrominance Processing
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Solution Overview
Problem
Existing digital imaging systems using Bayer sensors face challenges in efficiently determining color components of pixels, leading to artifacts like color fringes and stepped structures due to interpolation methods, which also increase computing time and memory inefficiencies.
Innovation Solution
The solution involves using high-resolution interpolation in luminance and low-resolution interpolation in chrominance, along with edge sharpening only on high-resolution luminance, and employing specific filter matrices for efficient color component determination in a Bayer matrix, including a 5×5 neighborhood and accumulation matrices for reduced computing effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large neighborhood of pixels (e.g., 7×7 or 9×9) is used for interpolation to increase image quality, then interpolation quality improves, but computing time increases and memory access efficiency decreases
Solution Approach 1:
The patent segments the interpolation process into two independent stages: luminance interpolation using a 5×5 neighborhood and chrominance interpolation using a 3×3 neighborhood. This segmentation allows each stage to use an optimally sized window for its specific purpose, reducing the overall computational burden while maintaining interpolation quality.
Solution Approach 2:
The patent applies different interpolation strategies to different color components based on their importance to human vision. Luminance (Y) components use high-resolution interpolation with a larger 5×5 neighborhood to preserve detail, while chrominance (Cb, Cr) components use low-resolution interpolation with a smaller 3×3 neighborhood, matching the human eye's lower sensitivity to color detail.
2Measurement precision
If a large neighborhood of pixels (e.g., 7×7 or 9×9) is used for interpolation to increase image quality, then interpolation quality improves, but memory access efficiency decreases due to line-oriented access optimization
Solution Approach 1:
The patent segments the interpolation process into two independent stages: luminance interpolation using a 5×5 neighborhood and chrominance interpolation using a 3×3 neighborhood. This segmentation allows each stage to use an optimally sized window for its specific purpose, reducing the overall computational burden while maintaining interpolation quality.
Solution Approach 2:
The patent applies different interpolation strategies to different color components based on their importance to human vision. Luminance (Y) components use high-resolution interpolation with a larger 5×5 neighborhood to preserve detail, while chrominance (Cb, Cr) components use low-resolution interpolation with a smaller 3×3 neighborhood, matching the human eye's lower sensitivity to color detail.
3Measurement precision
If high-resolution interpolation is applied to all color channels, then image quality improves, but computational load increases significantly
Solution Approach 1:
The patent applies different interpolation strategies to different color components based on their importance to human vision. Luminance (Y) components use high-resolution interpolation with a larger 5×5 neighborhood to preserve detail, while chrominance (Cb, Cr) components use low-resolution interpolation with a smaller 3×3 neighborhood, matching the human eye's lower sensitivity to color detail.
Solution Approach 2:
The patent applies full high-resolution interpolation only where necessary (luminance channel) and uses reduced-resolution interpolation for the chrominance channels, implementing partial action rather than applying excessive processing to all channels uniformly.
4Measurement precision
If edge sharpening is applied to improve image sharpness and contours, then image quality improves, but artifacts may increase in homogeneous areas
Solution Approach 1:
The patent applies different processing to different regions of the image based on local characteristics. Edge sharpening is applied selectively only to the luminance component where detail is most important, while chrominance components receive different treatment, preventing color artifacts in homogeneous areas.
Data Source
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AI summary
The present invention relates to a device (100) for determining the color components of a pixel of a Bayer matrix, comprising: a filter (101) for determining a luminance estimate of the pixel by filtering pixels of the Bayer matrix that at least partially surround the pixel with a first resolution; a filter bank (103) for filtering the pixels of the Bayer matrix that at least partially surround the pixel with a second resolution in order to obtain a color component estimate of the pixel for each color channel, wherein the first resolution is higher than the second resolution; and a processor (105) for combining the color component estimates with the luminance estimate to obtain a color component of the pixel for each color channel.