Defective Pixel Detection Using Threshold Functions
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Solution Overview
Problem
Existing image processing systems struggle to detect and correct defective pixels, especially in 4×4 RGBIR color filter array patterns, which are crucial for various applications including safety features in electronic devices.
Innovation Solution
The system identifies the color channel of each image pixel, selects a threshold function based on the color channel, applies this function to nearest-neighbor pixel values to obtain a threshold value, and determines if the pixel is defective by comparing its value to the threshold value, generating statistics on defective pixels and their locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional VISS pipelines are used for processing, then processing capability for 2×2 CFA patterns is achieved, but processing capability for 4×4 RGBIR CFA patterns is lost
Solution Approach 1:
The patent segments the pixel array into 2×2 blocks and processes each block independently through threshold comparison operations. This segmentation allows the system to handle 4×4 RGBIR CFA patterns by applying the same 2×2 processing logic repeatedly, thereby achieving versatility without proportionally increasing overall system complexity
Solution Approach 2:
The patent creates a universal processing pipeline that can handle both 2×2 and 4×4 CFA patterns using the same core algorithms and hardware resources. The threshold comparison mechanism and neighbor pixel analysis work universally across different CFA configurations, eliminating the need for separate processing paths for different pattern sizes
2Adaptability or versatility
If fixed density color filter patterns are assumed, then processing simplicity is maintained, but support for variable density patterns like 4×4 RGBIR is lost
Solution Approach 1:
The patent implements dynamic adaptation to different CFA patterns by detecting the actual pattern configuration and adjusting the processing parameters accordingly. The system dynamically determines neighbor pixel relationships and threshold comparisons based on the specific 4×4 RGBIR pattern, allowing flexible handling of variable density patterns without sacrificing algorithmic clarity
3Reliability
If pre-processing analysis is not performed on raw domain images, then processing speed is maintained, but defective pixel detection capability is reduced
Solution Approach 1:
The patent performs preliminary threshold comparison operations on raw domain pixel values to identify defective pixels before subsequent processing stages. By conducting this analysis in the raw domain using efficient threshold logic, the system achieves reliable defective pixel detection without requiring complex pre-processing, thereby maintaining high processing throughput
Data Source
AI summary
Various disclosed embodiments relate to defective pixel detection and optimizing memory storage while carrying out defective pixel detection. An example, system for detecting defective pixels includes a memory to store threshold functions; and a defective pixel detector to apply, for each image pixel received, a select threshold function of the threshold functions to values of nearest-neighbor image pixels to obtain a threshold value; and determine, for each image pixel received, whether the image pixel is defective based on a comparison of a value of the image pixel to the threshold value. A statistics generator receives each image pixel that is determined to be defective; and determines a number of defective image pixels in a specified unit of image pixels and a location of each defective image pixel in the specified unit.