Image Processing Apparatus for Noise Point Defect Cause Determination
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Solution Overview
Problem
Determining the cause of image defects in image forming devices, such as printers, is challenging due to the complexity of identifying issues like noise points, vertical stripes, and horizontal stripes, which require skilled analysis and often involve various components like photoconductors, charging devices, and transfer portions.
Innovation Solution
An image processing method and apparatus that utilize a processor to analyze a test image by extracting noise points and determining their cause based on edge strength, degree of flatness, and pixel value distribution, allowing for the identification of defects like abnormal developing, carrier developing, and waste toner dropping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skilled analysis is used to determine the cause of image defects, then determination accuracy is improved, but operation complexity increases
Solution Approach 1:
The image processing apparatus automatically performs defect detection and cause determination without requiring skilled operators. The system self-analyzes the test image by extracting noise points, calculating edge strengths, and comparing pixel value distributions to automatically identify defect causes such as abnormal developing or carrier developing issues.
Solution Approach 2:
The patent replaces manual skilled analysis with an automated image processing system that uses computational methods. Instead of relying on human expertise to visually inspect and diagnose image defects, the system uses processors to automatically extract features, calculate metrics like edge strength and flatness degree, and determine defect causes through algorithmic analysis.
2Measurement precision
If multiple parameters are analyzed to determine noise point causes, then determination accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the defect analysis process into distinct computational stages: noise point extraction, edge strength calculation, flatness degree computation, and pixel value distribution analysis. Each stage processes specific features independently, allowing the system to handle multiple parameters systematically without overwhelming complexity.
Solution Approach 2:
The system transforms the test image into multiple parameter representations including edge strength values, flatness degree metrics, and pixel value distribution statistics. By converting visual defect characteristics into quantifiable parameters, the system enables accurate cause determination through mathematical comparison and analysis.
Data Source
AI summary
A processor determines a cause of an image defect based on a test image that is obtained through an image reading process performed on an output sheet output from an image forming device. The processor generates an extraction image by extracting, from the test image, a noise point that is a type of image defect. Furthermore, the processor determines a cause of the noise point by using at least one of, in the extraction image: an edge strength of the noise point; a degree of flatness of the noise point; and a pixel value distribution of a transverse pixel sequence that is a pixel sequence traversing the noise point.