Color Interpolation for Image Sensors with Light Interference Compensation
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Solution Overview
Problem
Conventional image sensors experience image quality degradation due to light interference effects, particularly in mega-pixel sensors with small pixel sizes, leading to noise formation and uniform pattern distortion during image interpolation, which existing mean and median filters fail to completely remove.
Innovation Solution
A color interpolation method for CMOS image sensors that calculates average values for green pixels on odd and even rows, compares these values with a standard noise value, and applies either a normal or compensation interpolation method based on the comparison to address channel value differences and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pixel size is decreased to increase integration, then image sensor integration is improved, but light interference effect increases
Solution Approach 1:
The patent segments green pixels into odd-row green pixels and even-row green pixels, treating them separately through different interpolation paths. This segmentation allows independent handling of channel value differences that arise from light interference, enabling precise compensation for each group while maintaining high integration density.
Solution Approach 2:
The patent changes the interpolation parameter selection based on the comparison between channel value difference and noise standard deviation. When channel value difference exceeds noise threshold, compensation interpolation is applied; otherwise, normal interpolation is used. This dynamic parameter adjustment optimizes image quality while accommodating light interference effects in highly integrated sensors.
2Device complexity
If conventional interpolation method is used, then processing simplicity is maintained, but image quality degrades due to noise and uniform pattern
Solution Approach 1:
The patent introduces a dynamic interpolation method that adapts to local image characteristics by comparing channel value differences with noise standard deviation. The interpolation approach switches between normal and compensation modes based on real-time analysis, optimizing image quality without requiring complex fixed processing for all cases.
Solution Approach 2:
The patent applies different interpolation strategies to different regions based on local characteristics. By calculating channel value differences and comparing them with noise thresholds locally, the system applies compensation interpolation only where needed (when light interference is detected), rather than uniformly across the entire image, thus balancing quality and complexity.
3Ease of manufacture
If pixel size is decreased, then production cost is reduced, but light interference effect increases
Solution Approach 1:
The patent uses neighboring pixel values to compensate for light interference effects in affected pixels. By copying and combining information from adjacent pixels through the compensation interpolation formula, the system corrects interference artifacts without requiring additional physical pixels or complex hardware modifications, thus maintaining cost-effectiveness.
4Device complexity
If green pixel values from odd and even rows are not differentiated, then processing simplicity is maintained, but channel value variation causes image degradation
Solution Approach 1:
The patent segments green pixels into odd-row green pixels and even-row green pixels, calculating channel value differences separately for each group. This segmentation reveals and addresses the systematic variations between rows caused by light interference, enabling precise compensation while maintaining relatively simple processing through structured separation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively prevents image degradation by minimizing channel value differences, improving image quality by reducing noise and maintaining accurate color interpolation across the pixel array.
Implementation Method 1
pixels of the image sensors detect different brightness and wavelength coming out of each individual subject and read the detected brightness and wavelength into an electrical value
Data Source
AI summary
A color interpolation method of an image sensor including a pixel array which green, red and blue pixels are arrayed in Bayer pattern is provided. The method includes: calculating a first average value of first values filtered by green pixels on even rows and a second average value of a second values filtered by green pixels on odd rows; comparing a value difference between the first average value and the second average value with a standard value; and performing one of a normal interpolation method and a compensation interpolation method with respect to the values filtered by the green pixels according to the comparison.