Pixel Correction Method Using Defect Class and Adjacent Signals
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Solution Overview
Problem
Conventional methods for correcting defective pixels in image sensors discard all defective pixels uniformly, leading to high correction effort and potential impairment of image quality, as they do not account for varying degrees of defect severity and environmental dependencies.
Innovation Solution
A method that determines whether to correct each pixel signal based on its defect class and the signals of adjacent pixels, replacing only those that would result in improved image quality, using a twofold dependence on defect class and environmental context to differentiate correction treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If all defective pixels are uniformly discarded and corrected by interpolation, then the correction process is simplified, but the correction effort increases and image quality may be impaired due to unnecessary corrections
Solution Approach 1:
The patent applies local quality by differentiating correction strategies based on the specific defect characteristics of each pixel. Instead of uniform correction, pixels are categorized into defect classes (e.g., hot pixels, dead pixels, noisy pixels) and subjected to appropriate correction methods only when necessary. This selective approach reduces unnecessary corrections while maintaining image quality.
Solution Approach 2:
The patent changes the parameter of defect severity by introducing a threshold-based determination mechanism. The correction decision is made based on comparing the defect characteristic against a threshold value, allowing the system to adaptively select which pixels require correction. This parameter-based approach optimizes the balance between correction effort and image quality.
2Ease of operation
If all defective pixels are uniformly discarded, then the correction process is straightforward, but usable pixel information is lost and image quality is unnecessarily impaired
Solution Approach 1:
The patent implements local quality by assigning different defect classes to pixels based on their specific characteristics. Pixels are not uniformly discarded but rather classified into categories such as unusable, usable with limitations, or usable without limitations. This allows the system to preserve usable pixel information while still correcting problematic pixels.
Solution Approach 2:
The patent applies partial action by selectively correcting only those pixels that meet the correction criterion based on their defect class and characteristic values. Instead of correcting all defective pixels uniformly, the system performs partial correction on a case-by-case basis, preserving information from pixels that do not require correction.
3Manufacturing precision
If pixels are differentiated by defect class, then correction can be tailored to defect severity, but the correction decision becomes more complex without considering environmental context
Solution Approach 1:
The patent merges multiple decision factors by combining defect class classification with defect characteristic evaluation and environmental context (adjacent pixel signals). The correction decision is made based on a comprehensive assessment that integrates pixel-specific defect information with surrounding environmental data, providing a more accurate and context-aware correction strategy.
Solution Approach 2:
The patent incorporates feedback by using the signals of adjacent pixels to inform the correction decision for a given pixel. The determination of whether to correct a pixel depends on both its defect characteristics and the context provided by neighboring pixels, creating a feedback mechanism that adapts correction decisions to local image conditions.
4Productivity
If correction is based only on defect class, then processing is efficient, but corrections may worsen image quality when environmental context is not considered
Solution Approach 1:
The patent implements feedback by incorporating adjacent pixel signals into the correction decision process. The system uses the local image context provided by neighboring pixels to determine whether a correction is appropriate, creating a feedback loop that prevents erroneous corrections and maintains image quality while preserving processing efficiency.
Solution Approach 2:
The patent applies preliminary action by pre-classifying pixels into defect classes and pre-determining correction criteria based on defect characteristics. This preliminary classification allows the system to efficiently process pixels while maintaining the ability to make context-aware correction decisions, balancing speed and accuracy.
Data Source
AI summary
In a method for correcting defective pixels of an image sensor which has a plurality of pixels for generating respective exposure dependent pixel signals, a defect characteristic for each pixel is associated with the image sensor and comprises information at least on whether the pixel is unusable, is usable without limitations or corresponds to one of a plurality of predefined defect classes. The method comprises a determination being made after the generation of the pixel signals for each pixel, at least when the defect characteristic of the pixel does not correspond to an unusable pixel or to a pixel which can be used without limitations, in dependence on at least the associated defect class of the pixel and on pixel signals of a plurality of adjacent pixels, whether the generated pixel signal of the pixel should be corrected, with the generated pixel signal of the pixel being replaced with a replacement value if this is the case.