Dynamic Edge-Preserving Defect Pixel Correction for Image Sensors
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Solution Overview
Problem
Conventional image processing techniques are inflexible and ineffective in dynamically correcting defect pixels in image sensors, particularly those that occur intermittently, leading to variations in image quality and resolution.
Innovation Solution
A defect pixel correction system that computes neighborhood statistics to dynamically detect defective pixels, replacing them with either the central tendency of neighbor pixels in flat regions or the average of values from the minimum edge in non-flat regions, while preserving local edges and maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional static pixel correction is used, then device complexity is reduced, but adaptability to intermittent defects deteriorates
Solution Approach 1:
The patent implements dynamic defect detection by computing neighborhood statistics (mean and standard deviation) for each pixel in real-time during image processing. This dynamic approach allows the system to adapt to intermittent defects that static correction methods cannot detect, as the correction parameters are recalculated for every frame based on current image content rather than using fixed pre-determined values.
Solution Approach 2:
The system performs self-correction by automatically detecting defective pixels through neighborhood statistical analysis and replacing them with corrected values computed from surrounding pixels. This self-service mechanism eliminates the need for external manual intervention or complex external correction systems, maintaining low device complexity while achieving high adaptability.
2Adaptability or versatility
If conventional dynamic pixel correction is used, then adaptability to intermittent defects is improved, but image quality deteriorates due to edge blurring
Solution Approach 1:
The patent applies different correction strategies based on local image characteristics. By analyzing neighborhood statistics and detecting edge regions versus flat regions, the system applies appropriate correction methods locally: preserving edges in edge regions and applying smoothing in flat regions. This local quality approach maintains high image quality while achieving adaptability to intermittent defects.
Solution Approach 2:
The system dynamically changes correction parameters based on local image content. The neighborhood standard deviation serves as a parameter that indicates whether a region is an edge or flat area. When the standard deviation exceeds a threshold, edge-preserving correction is applied; otherwise, smoothing correction is used. This parameter-based adaptation resolves the contradiction between defect correction and edge preservation.
3Device complexity
If simple replacement with neighbor pixel average is used, then device complexity is reduced, but image quality deteriorates due to edge blurring
Solution Approach 1:
The patent uses the neighborhood standard deviation as a dynamic parameter to determine the correction method. By comparing this parameter against a threshold, the system automatically selects between edge-preserving correction (when standard deviation is high) and simple averaging (when standard deviation is low). This parameter-driven approach maintains edge sharpness without requiring complex manual intervention, resolving the contradiction between simplicity and edge preservation.
Data Source
AI summary
Various technologies described herein pertain to defect pixel correction for image data collected by a pixel array of an image sensor. Neighborhood statistics for a given pixel from the image data are computed based on values of neighbor pixels of the given pixel from the image data. Whether the value of the given pixel is defective is detected based on the neighborhood statistics. The value of the given pixel is replaced when detected to be defective to output modified image data. Correction of the given pixel is a function of whether the given pixel is in a flat region or a non-flat region. When the given pixel is defective and in a non-flat region, a minimum edge across the given pixel is identified and the value of the given pixel is replaced with an average of values of neighbor pixels that belong to the minimum edge.