Adaptive Pixel Filter for Cross-Talk Compensation
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Solution Overview
Problem
Existing image processing techniques fail to effectively account for cross-talk effects when replacing anomalous pixel values in images, leading to suboptimal image quality due to the influence of neighboring pixels.
Innovation Solution
The use of adaptive decision-based filtering techniques, including calculating replacement pixel values using gradients and offsets, to reduce cross-talk effects, where replacement values are determined using average or median pixel values of neighbor pixels within a kernel, with adjustable thresholds for detecting anomalous pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pixel replacement techniques are used, then anomalous pixel values can be replaced, but cross-talk effects from neighboring pixels cause suboptimal image quality
Solution Approach 1:
The patent extracts and removes the harmful cross-talk component from the neighborhood pixel values before calculating the replacement value. By identifying and eliminating the cross-talk influence, the replacement pixel value is computed based on clean, uncontaminated neighborhood data, thereby resolving the contradiction between using neighborhood information and avoiding cross-talk contamination.
Solution Approach 2:
The patent introduces cross-talk compensation as an intermediary mechanism that mediates between the anomalous pixel and its neighbors. This intermediary process calculates and applies compensation values to neutralize the cross-talk effects, allowing the replacement algorithm to accurately reconstruct the anomalous pixel value without being influenced by contaminated neighborhood data.
2Device complexity
If simple average or median of neighbor pixels is used, then replacement is computationally simple, but cross-talk effects are not accounted for
Solution Approach 1:
The patent performs preliminary cross-talk compensation calculations before computing the final replacement pixel value. By pre-processing the neighborhood pixel values to remove cross-talk effects, the subsequent replacement calculation becomes a simple operation that maintains low computational complexity while achieving high accuracy through the preliminary cleanup of the input data.
Data Source
AI summary
Techniques are provided to detect and/or replace anomalous pixels. In one example, a method includes receiving an image frame comprising a plurality of pixels having associated pixel values. The method also includes selecting a kernel of the pixels comprising a center pixel and a plurality of neighbor pixels. The method also includes, if the center pixel value exhibits an anomalous pixel condition, calculating a replacement pixel value using a gradient associated with at least a subset of pixel values of the neighbor pixels. Additional methods and systems are also provided.