Bad Pixel Cluster Detection via Lookup Table Thresholds
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Solution Overview
Problem
Accurate detection and correction of bad pixel clusters in portable computing devices are limited by available processing resources, which affects image quality.
Innovation Solution
A system with a bad pixel correction module that determines whether test pixels exceed or fall below the values of surrounding pixels by using threshold values from a lookup table, allowing for efficient detection and correction of bad pixel clusters using a relatively small number of processing steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bad pixel detection methods are used, then detection accuracy is improved, but processing resource consumption increases
Solution Approach 1:
The detection process is divided into two distinct stages: cluster detection (identifying groups of bad pixels) and individual bad pixel detection. This segmentation allows the system to prioritize cluster detection which consumes fewer resources, while still achieving comprehensive coverage. The lookup table approach further segments the computation by pre-calculating threshold values, reducing real-time processing demands.
Solution Approach 2:
The lookup table is pre-computed and stored before actual image processing occurs. This preliminary action eliminates the need for complex real-time calculations during detection, allowing the system to quickly compare pixel values against pre-determined thresholds. This significantly reduces processing resource consumption during the actual detection phase while maintaining detection accuracy.
2Measurement precision
If complex detection algorithms are used, then detection precision is improved, but device complexity increases
Solution Approach 1:
The algorithm is segmented into simple, discrete steps: reading pixel values, comparing against lookup table thresholds, and identifying clusters based on spatial relationships. Each step is computationally simple, avoiding the need for complex mathematical operations while achieving accurate detection through the structured approach.
Solution Approach 2:
The lookup table serves as an intermediary structure that mediates between the raw pixel data and the detection logic. Instead of implementing complex detection algorithms directly, the system uses the lookup table to translate pixel value patterns into detectable cluster signatures, simplifying the overall processing complexity.
3Measurement precision
If more processing steps are added, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The lookup table is constructed in advance during system initialization or manufacturing, performing the computationally intensive threshold calculations beforehand. During actual image processing, the system only needs to perform simple value lookups and comparisons, dramatically reducing processing time while maintaining the accuracy benefits of the comprehensive detection approach.
Solution Approach 2:
The system performs detection on a representative sample of pixels using the cluster-based approach, which covers multiple pixels simultaneously. This partial action on clusters rather than individual pixels achieves comprehensive detection coverage with fewer processing steps, reducing overall processing time while maintaining high detection accuracy.
Data Source
AI summary
Systems and methods of bad pixel cluster detection are disclosed. In a particular embodiment, a system is disclosed that includes a bad pixel correction module coupled to receive image data and adapted to perform a bad pixel cluster detection process. The bad pixel correction module includes logic to determine whether two test pixels have values that exceed a representative value of a group of surrounding pixels by more than a threshold amount. The threshold amount is determined via a table lookup.