Bayer Sensor Interpolation via Dual-Filter Segmentation
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Solution Overview
Problem
Moiré effects and spectral aliasing in color image sensors lead to artefacts like false colors due to sub-sampling operations during interpolation, particularly in images with abrupt spatial variations in intensity or color, as existing interpolation methods like bilinear interpolation and low-pass filtering are inadequate.
Innovation Solution
The method involves using two distinct interpolation filters: a low-pass filter for color components and a high-pass filter for luminance, with the results combined to improve interpolation accuracy, specifically using a bilinear filter convolved with a low-pass and high-pass filter to create low- and high-frequency interpolation filters, applied separately to each color component and then combined.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If simple interpolation functions like linear or bilinear interpolation are used, then the computing power and memory requirements are reduced, but moiré effects and spectral aliasing occur due to sub-sampling operations
Solution Approach 1:
The patent segments the interpolation process into two distinct stages: first performing color-component interpolation on individual color channels, then performing luminance interpolation on the combined signal. This segmentation allows each interpolation stage to be optimized for its specific purpose, reducing overall computational complexity while maintaining image quality and avoiding moiré effects.
Solution Approach 2:
The patent changes the interpolation parameters by applying different interpolation methods to different signal components. Specifically, it uses color-component interpolation with specific weighting coefficients for chrominance, then applies luminance interpolation with different coefficients for luminance, allowing optimal parameter selection for each aspect of image reconstruction.
2Object-affected harmful factors
If sophisticated interpolation functions like cubic interpolation are used, then moiré effects and spectral aliasing are reduced, but substantial computing power and memory are required
Solution Approach 1:
By dividing the interpolation into color-component and luminance stages, the patent achieves sophisticated interpolation results without requiring a single complex cubic interpolation algorithm. Each stage uses simpler, optimized interpolation methods that together produce high-quality results with reduced computational burden.
Solution Approach 2:
The patent optimizes interpolation parameters by using different weighting coefficients for different color components and for luminance. This parameter optimization allows achieving sophisticated interpolation quality with computationally efficient algorithms, avoiding the need for memory-intensive cubic interpolation.
3Measurement precision
If color-component interpolation is performed separately for each color, then colour accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent merges the color-component interpolation results with luminance interpolation in a combined processing stage. By interpolating color components separately first, then combining them with luminance information in a unified framework, the patent achieves accurate color reconstruction while managing processing complexity through structured organization of operations.
Data Source
AI summary
The invention relates to the processing operation of interpolating the colours of a Bayer mosaic image sensor. A first elementary matrix filter, which is a bilinear interpolation filter, of size m×m, m being an odd number larger than or equal to 3, a low-pass matrix filter of size n×n, n being an odd number larger than or equal to 3, and a high-pass matrix filter, complementary to the low-pass filter, of size n×n, are defined. The first matrix filter is convoluted with the low-pass filter, resulting in a low-frequency interpolation filter of size (m+n−1)×(m+n−1), and the first matrix filter is convoluted with the high-pass filter, resulting in a high-frequency interpolation filter of size (m+n−1)×(m+n−1). The matrix of digital signals arising from the pixels is filtered separately, using the pixels of each colour, by the low-frequency interpolation filter. The complete matrix of signals is filtered using the high-frequency interpolation filter. The result of the low-frequency filtering operation and the result of the high-frequency filtering operation are added together, for each pixel, in order to obtain a numerical value of a given colour of that pixel.