Confidence-Based Color Interpolation for Edge Sharpness
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Solution Overview
Problem
Conventional color interpolation methods in digital image capture devices, such as those using the Bayer pattern, often result in reduced sharpness of edges due to averaging neighboring pixel values, and require complex computations that are energy-intensive and costly, particularly in battery-powered devices like cell phones.
Innovation Solution
A method involving two directional color interpolation functions, one vertical and one horizontal, with metrics to determine weighting factors for a weighted sum of missing color sub-pixel values, reducing the number of full multiplication operations required and maintaining edge sharpness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color interpolation methods average neighboring pixel values, then missing color sub-pixel values can be determined, but edge sharpness is reduced
Solution Approach 1:
The patent applies different interpolation strategies for different regions. When an edge is detected (through metrics comparing pixel value differences), the interpolation function is modified to preserve edge sharpness by reducing the influence of pixels across the edge. This local adaptation allows accurate color interpolation while maintaining edge sharpness where needed.
Solution Approach 2:
The patent uses dynamic interpolation functions that adapt based on detected edges. The interpolation function is not fixed but changes based on edge detection metrics. When edges are present, the function adjusts to minimize blending across edges; when no edges are present, standard averaging is used. This dynamic adaptation resolves the contradiction between interpolation accuracy and edge sharpness.
2Shape
If complex computations are used to maintain edge sharpness, then edge sharpness is preserved, but computational complexity and energy consumption increase
Solution Approach 1:
The patent applies complex edge-preserving interpolation only partially - specifically only when edges are detected in the image data. For regions without edges, simpler interpolation methods are used. This partial application of complex computations only where necessary maintains edge sharpness while minimizing overall computational complexity and energy consumption.
Solution Approach 2:
The patent changes the parameters of the interpolation function based on edge detection metrics. By adjusting interpolation weights and function parameters dynamically based on local image characteristics, the system achieves edge sharpness without requiring consistently complex computations, thereby reducing overall computational burden.
3Measurement precision
If more multiplication operations are used for weighted sum calculations, then interpolation accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent performs full weighted sum calculations with multiple multiplication operations only when edges are detected and require special handling. For non-edge regions, simplified interpolation without extensive multiplication is used. This partial application of computationally intensive operations maintains interpolation accuracy where needed while minimizing overall energy consumption.
Data Source
AI summary
A color interpolation method uses a first interpolation function (F1) to obtain a first missing color sub-pixel value for a pixel of interest and uses a second interpolation function (F2) to obtain a second missing color sub-pixel value for the pixel of interest. First metric (V) indicative of an edge extending in a first direction (D1) is obtained. Second metric (H) indicative of an edge extending in a second direction (D2) is obtained. The two metrics are used to generate first and second weighting factors (k1, k2). A confidence factor value can be used to place more emphasis on one metric versus the other metric in the determination of the weighting factors. In one embodiment, the sub-pixel value being interpolated is the weighted sum of the first weighting factor multiplied by the first missing color sub-pixel value plus the second weighting factor multiplied by the second missing color sub-pixel value.