Pixel Defect Detection via Isotropic and Directional Dispersion Analysis
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Solution Overview
Problem
Existing methods for detecting defective pixel values in digital images are not effective in distinguishing between isotropic and directional variations, leading to inaccurate identification and correction of pixel defects.
Innovation Solution
A method that calculates isotropic and directional dispersion difference values for each pixel by comparing pixel errors with isotropic and directional dispersions, respectively, and determines pixel defects based on threshold comparisons that vary with edge response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pixel defect detection methods are used, then pixel errors can be detected, but the accuracy of distinguishing between isotropic and directional defects is insufficient
Solution Approach 1:
The patent segments the defect detection process into two distinct pathways: isotropic defect detection and directional defect detection. This segmentation allows the system to apply different detection strategies tailored to different types of pixel defects, improving overall detection accuracy by addressing each defect type with its optimal detection method
Solution Approach 2:
The patent introduces directional analysis as an additional dimension to traditional defect detection. By calculating dispersion values in multiple directions (horizontal, vertical, diagonal) and comparing them, the system transforms the detection process from scalar to vector-based analysis, enabling differentiation between isotropic and directional defects
2Measurement precision
If isotropic and directional dispersion calculations are performed for each pixel, then defect identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by calculating dispersion values using only neighboring pixels within a defined window around each pixel under test. This localized approach reduces the computational scope from global image analysis to local neighborhood analysis, significantly reducing computational power requirements while maintaining detection accuracy
Solution Approach 2:
The system performs partial action by selectively applying directional dispersion calculations only when isotropic defect detection is insufficient or when directional patterns are suspected. This selective application of computational resources optimizes the balance between detection accuracy and computational power consumption
3Manufacturing precision
If threshold values are adjusted based on edge response, then edge detail preservation is improved, but detection sensitivity varies across different image regions
Solution Approach 1:
The patent implements dynamic threshold adjustment based on local edge response characteristics. By calculating edge response metrics in different image regions and adapting thresholds accordingly, the system achieves high detection sensitivity in uniform regions while preserving edge details in high-contrast regions, making the detection process adaptive to local image characteristics
Data Source
AI summary
A method for detecting defective pixel values determines an isotropic dispersion difference value by determining a ratio or difference between a pixel error and an isotropic dispersion, where the isotropic dispersion is a measure of how much pixel values in a set of neighbouring pixel values uniformly distributed around the pixel under consideration vary. The method compares the isotropic dispersion difference value to an isotropic threshold. A directional dispersion difference value is found by determining a ratio or difference between a pixel error and a directional dispersion, wherein the directional dispersion is a weighted measure of how much pixel values in a set of neighbouring pixel values around the pixel under consideration in a given direction vary. The directional dispersion difference value is compared to a directional threshold and it is determined that the pixel under consideration is defective based on at least one of the comparison results.