Adaptive Anomalous Pixel Filtering for Cross-Talk Reduction
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Solution Overview
Problem
Existing image processing techniques fail to adequately address anomalous pixels caused by gain calibration issues, sensor defects, and manufacturing tolerances, leading to 'salt and pepper noise' that diminishes image quality.
Innovation Solution
Adaptive decision-based filtering techniques are employed to calculate replacement pixel values using neighbor pixel averages and gradients, with optional offsets, to correct anomalous pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pixel replacement techniques are used, then anomalous pixels can be identified and replaced, but cross-talk effects from neighboring pixels contaminate the replacement values and reduce measurement precision
Solution Approach 1:
The patent extracts and removes the anomalous pixel from the kernel processing, preventing its contaminated value from affecting neighbor pixels. By identifying the anomalous pixel through statistical analysis and excluding it from the replacement calculation, the system obtains cleaner replacement values that are not influenced by the defective pixel's cross-talk effects.
Solution Approach 2:
The patent introduces an intermediary process that calculates replacement pixel values through multiple passes and uses statistical methods (median, mean, gradient analysis) to mediate between the anomalous pixel and its neighbors. This intermediary approach filters out cross-talk contamination before establishing the final replacement value.
2Productivity
If simple average filtering is used to replace anomalous pixels, then processing is computationally efficient, but the replacement values are contaminated by cross-talk from neighboring pixels
Solution Approach 1:
The patent performs preliminary identification of anomalous pixels through statistical analysis before conducting replacement operations. By pre-processing the image to flag anomalous pixels and analyze their kernels, the system prepares clean replacement values in advance, avoiding the need for complex real-time calculations during the replacement phase.
Solution Approach 2:
The patent employs dynamic, adaptive processing that adjusts the replacement strategy based on local image characteristics. Different replacement methods (median, mean, gradient-based) are selectively applied depending on the detected patterns and cross-talk severity in each kernel, optimizing both accuracy and efficiency for different regions.
3Measurement precision
If multiple processing passes are used to improve anomalous pixel correction, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent applies partial processing by focusing computational resources only on kernels containing anomalous pixels rather than processing the entire image uniformly. By identifying and treating only the affected regions with multiple passes, the system achieves high precision for critical areas while maintaining overall processing efficiency.
Data Source
AI summary
Techniques are provided to detect and/or replace anomalous pixels. In one example, a method includes receiving an image frame having a plurality of pixels having associated pixel values. The method also includes selecting a kernel of the pixels having a target pixel and a plurality of neighbor pixels. If the target pixel value exhibits an anomalous pixel condition, the method includes calculating a replacement pixel value associated with at least a subset of pixel values of the neighbor pixels. Additional methods and systems are also provided.