Anomalous Pixel Processing for Selective Noise Correction
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Solution Overview
Problem
Existing image processing techniques inefficiently allocate resources and reduce accuracy due to the lack of discretion in handling anomalous pixels with varying degrees of noise, leading to inefficient processing and inaccurate pixel values.
Innovation Solution
Implementing selective spatial pattern noise reduction and linearity processing techniques to identify and correct or replace anomalous pixels based on the magnitude of their anomalies, optimizing resource usage by applying more intensive processing only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single processing technique is applied to all pixels without discretion, then the processing pipeline is simple, but processing resources are allocated inefficiently and accuracy is reduced
Solution Approach 1:
The patent applies different processing techniques to different pixels based on their anomaly characteristics. Pixels with small fixed pattern noise receive correction term processing, while pixels with large fixed pattern noise receive replacement processing. This local differentiation optimizes both accuracy and resource allocation by matching the processing method to the specific needs of each pixel.
Solution Approach 2:
The patent segments the pixel population into distinct groups based on their noise characteristics and applies specialized processing to each segment. By dividing pixels into those needing correction versus those needing replacement, the system achieves more efficient resource utilization and improved overall accuracy without requiring complete redesign of the processing architecture.
2Measurement precision
If intensive processing is applied to all pixels, then accuracy is maximized, but processing resources are wasted on pixels that do not require intensive processing
Solution Approach 1:
The system determines the processing intensity required for each pixel based on its specific anomaly characteristics. Pixels with minor issues receive lightweight correction processing, while only pixels with severe anomalies receive intensive replacement processing. This localized quality adjustment ensures processing resources are consumed proportionally to the actual need.
Solution Approach 2:
The patent applies partial processing intensity matched to the severity of each pixel's anomaly. Rather than uniformly applying maximum processing to all pixels, the system applies only the necessary level of processing to each pixel, avoiding waste on pixels that can be adequately handled by lighter processing methods.
3Measurement precision
If correction terms are applied to all pixels, then small fixed pattern noise is reduced, but large fixed pattern noise cannot be effectively corrected and processing resources are wasted
Solution Approach 1:
The patent differentiates the processing approach based on the magnitude of fixed pattern noise in each pixel. For pixels with small fixed pattern noise, correction terms are applied to maintain accuracy. For pixels with large fixed pattern noise, the system switches to replacement processing, as correction terms are insufficient and wasteful for such severe cases.
Solution Approach 2:
The system applies partial correction action only where appropriate (for small noise cases) and reserves replacement action for cases where it is necessary (large noise cases). This selective application of processing intensity ensures that correction terms are not wasted on pixels where they would be ineffective, while also avoiding the resource consumption of replacement processing for pixels where correction would suffice.
Data Source
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AI summary
Techniques are provided to identify, correct, and/or replace anomalous pixels. In one example, a method includes receiving an image frame comprising a plurality of pixels arranged in a plurality of rows and columns. The pixels comprise image data associated with a scene and fixed pattern noise introduced by an imaging device. The method also includes performing a first process on a first set of the pixels to determine associated correction terms configured to reduce the fixed pattern noise, and applying the correction terms to the first set of the pixels in response to the first process. The method also includes performing a second process on a second set of the pixels to determine whether to replace the second set of the pixels to reduce the fixed pattern noise, and replacing at least a subset of the second set of the pixels in response to the second process. Additional methods and systems are also provided.