Dynamic Pixel Defect Correction Using Confidence History
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Solution Overview
Problem
Conventional image processing systems rely on static defect tables for identifying defective pixels, which are not adaptive to changing conditions and may not accurately correct patterned defect pixels, leading to suboptimal image quality.
Innovation Solution
An image signal processor (ISP) with a dynamic pixel defect correction component that tracks defect history and uses a defect confidence value to identify and correct defective pixels, including patterned defect pixels, by comparing pixel values to neighbor pixels and updating a defect pixel location table over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static defect table is used to identify defective pixels, then the system is simple to implement, but the accuracy of defect detection is insufficient and cannot adapt to changing conditions
Solution Approach 1:
The patent transforms the static defect table into a dynamic system by continuously updating defect confidence values based on historical frame analysis. The defect pixel location table is no longer fixed but evolves over time, adapting to changing sensor conditions and improving detection accuracy while maintaining manageable complexity through systematic updates.
Solution Approach 2:
The system implements feedback by analyzing each processed frame to update defect confidence values in the defect pixel location table. This closed-loop approach uses detection results from current frames to refine the defect table for future frames, creating a self-improving system that adapts to changing conditions.
2Measurement precision
If dynamic defect detection is performed for every frame, then the accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing comprehensive dynamic defect detection only when necessary, while relying on the maintained defect pixel location table for routine frames. The system balances between full dynamic analysis and table-based correction, reducing processing time while maintaining accuracy through selective application of detection methods.
Solution Approach 2:
The system performs preliminary action by maintaining an updated defect pixel location table from previous frames, which provides a head start for current frame processing. This pre-computed information reduces the computational burden of real-time defect detection and allows faster processing of each individual frame.
3Adaptability or versatility
If a static defect table is used, then the system is stable, but it cannot adapt to patterned defect pixels or changing sensor conditions
Solution Approach 1:
The patent introduces dynamics into the previously static defect table system, allowing it to adapt to patterned defect pixels and changing sensor conditions. The defect pixel location table evolves by incorporating defect confidence values from multiple frames, enabling the system to respond to varying conditions while maintaining operational stability through systematic update procedures.
Solution Approach 2:
The system uses feedback from continuous frame analysis to adapt the defect pixel location table to changing sensor conditions and patterned defects. This feedback mechanism allows the system to learn from historical data and adjust its defect detection parameters, achieving adaptability while maintaining stability through controlled updates based on confidence thresholds.
Data Source
AI summary
An image signal processor may include a pixel defect correction component that tracks defect history for frames captured by an image sensor and applies the history when identifying and correcting defective pixels in a frame. The component maintains a defect pixel location table that includes a defect confidence value for pixels of the image sensor. The component identifies defective pixels in a frame, for example by comparing each pixel's value to the values of its neighbor pixels. If a pixel is detected as defective, its defect confidence value may be incremented. Otherwise, the value may be decremented. If a pixel's defect confidence value is over a defect confidence threshold, the pixel is considered defective and thus may be corrected. If a pixel's defect confidence value is under the threshold, the pixel is considered not defective and thus may not be corrected even if the pixel was detected as defective.