Outlier Pixel Detection Using Spatial and Temporal Filters
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Solution Overview
Problem
Video image sensors often produce defective pixels that significantly contrast with surrounding pixels, leading to visually distracting artifacts in video streams due to temporary or permanent defects.
Innovation Solution
A circuit comprising a spatial filter and a temporal filter identifies outlier pixels by computing pixel differences within frames and tracking divergence across successive frames, dynamically adding and removing pixels from a candidate list, and using interpolation to replace outlier pixels with nearby pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor defects are detected and corrected using simple methods, then processing speed is maintained, but detection precision of outlier pixels deteriorates
Solution Approach 1:
The filtering system is divided into two independent modules: spatial filter and temporal filter. The spatial filter segments the detection task by comparing each pixel with its spatial neighbors to identify candidate outliers, while the temporal filter segments the validation task by tracking candidate pixels across multiple frames to confirm true outliers. This segmentation allows each module to specialize in one aspect of detection, improving overall precision without requiring a single complex system.
Solution Approach 2:
The patent transitions from single-frame spatial analysis to multi-frame temporal analysis by adding the time dimension. The temporal filter examines candidate pixels across N successive frames, using temporal consistency to distinguish true outliers from normal variations. This dimensional expansion significantly improves detection precision by filtering out false positives that appear in only one frame.
2Reliability
If all pixels are processed to identify outliers, then detection completeness improves, but processing time increases
Solution Approach 1:
The spatial filter performs preliminary screening by comparing each pixel with its spatial neighbors and adding suspicious pixels to a candidate list. This preliminary action filters out the majority of normal pixels before the more time-consuming temporal analysis, so only candidate outliers undergo multi-frame verification. This two-stage approach maintains high detection reliability while significantly reducing overall processing time.
Solution Approach 2:
The temporal filter applies full verification to only the candidate pixels identified by the spatial filter, rather than verifying all pixels. This partial action approach focuses computational resources on the small subset of suspicious pixels, maintaining high detection reliability for outliers while avoiding the time cost of processing every pixel through the complete filtering pipeline.
3Measurement precision
If threshold value is lowered to detect more outliers, then detection sensitivity improves, but false positive rate increases
Solution Approach 1:
The temporal filter provides feedback by tracking candidate pixels across multiple frames and checking for temporal consistency. True outliers typically maintain their abnormal characteristics across frames, while false positives from low threshold spatial filtering tend to disappear in subsequent frames. This feedback mechanism allows the system to use a lower spatial threshold for sensitivity while maintaining high reliability through temporal validation.
Solution Approach 2:
The system prepares for potential false positives by requiring temporal confirmation before finalizing outlier status. The candidate list acts as a buffer zone where suspicious pixels are held for verification across N frames. This beforehand cushioning prevents premature classification of false positives as true outliers, allowing sensitive spatial filtering without sacrificing reliability.
4Reliability
If candidate list size is increased to store all suspicious pixels, then detection completeness improves, but memory usage increases
Solution Approach 1:
The spatial filter identifies only the most suspicious pixels and adds them to the candidate list, rather than including all pixels that might potentially be outliers. This partial action approach focuses memory resources on the small subset of high-probability candidates, maintaining detection completeness for true outliers while minimizing memory consumption by excluding clearly normal pixels from the candidate list.
Data Source
AI summary
Circuits, systems, and methods for processing outlier pixels include a spatial filter and a temporal filter. The spatial filter is configured to compute a pixel difference for each pixel as a function of a pixel value of the pixel and pixel values of nearby pixels within each frame. The spatial filter is configured to dynamically add the pixel to a candidate list when the pixel difference exceeds a threshold value. The temporal filter dynamically removes a pixel from the candidate list when there is a divergence of a pixel value of the pixel in successive frames. The temporal filter determines a pixel in the candidate list is an outlier pixel when there is no such divergence in the successive frames.