Image Processing for Noise Point Cause Identification
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Solution Overview
Problem
Determining the cause of image defects in electrophotographic image forming devices, such as vertical stripes, horizontal stripes, and noise points, is challenging due to the complexity of the processes and requires skilled analysis to differentiate between waste toner dropping and carrier developing.
Innovation Solution
An image processing method and apparatus that utilize a test image to identify the cause of noise points by determining the degree of overlapping between the original drawing part and the noise point, employing a processor to extract feature images and determine the cause between waste toner dropping and carrier developing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skilled analysis is used to determine the cause of image defects, then accuracy in identifying waste toner dropping versus carrier developing is improved, but the complexity of the process and requirement for expert knowledge increases
Solution Approach 1:
The patent segments the image defect analysis into distinct processing steps: extracting test images, detecting noise points, identifying original drawing parts, calculating overlap degrees, and determining causes based on threshold comparisons. This segmentation transforms a complex skilled analysis into systematic automated processing steps that can be executed by a processor without requiring expert knowledge.
Solution Approach 2:
The patent introduces an intermediary computational process that acts as a mediator between the raw test image and the final cause determination. The overlap degree calculation serves as an intermediary metric that translates visual characteristics into quantifiable data, enabling automated decision-making without requiring skilled human analysis.
2Ease of operation
If automated processing is implemented to determine image defect causes, then the need for skilled analysis is reduced, but the complexity of the processing system increases
Solution Approach 1:
The system performs self-service by automatically executing the entire defect analysis process without human intervention. The processor autonomously extracts test images, detects noise points, identifies original drawing parts, calculates overlap degrees, and determines causes based on predefined thresholds, making the system independent of skilled operators.
Solution Approach 2:
The patent transforms qualitative visual assessment into quantitative parameter measurement by calculating the overlap degree between noise points and original drawing parts. This parameter change enables automated comparison against threshold values, converting a skilled judgment task into a programmable decision process.
3Measurement precision
If the overlap degree method is used to differentiate waste toner dropping and carrier developing, then cause identification accuracy is improved, but the measurement and detection difficulty increases
Solution Approach 1:
The patent replaces manual visual inspection with automated image processing algorithms. The processor automatically detects noise points, identifies original drawing parts, and calculates overlap degrees using computational methods, substituting mechanical human analysis with automated digital processing that is more precise and consistent.
Solution Approach 2:
The system creates a digital copy of the test image and processes this copy to extract features and calculate overlap degrees. This copying approach allows multiple analyses of the same image data without affecting the original, enabling precise measurement through computational reconstruction and analysis.
Data Source
AI summary
A processor identifies an original drawing part that is originally drawn in a test image. Furthermore, the processor identifies a noise point in the test image. Furthermore, the processor determines which of predetermined two types of cause candidates is a cause of the noise point by determining a degree of overlapping between the original drawing part and the noise point. The two types of cause candidates are: waste toner dropping in which waste toner that has adhered to a transfer body that transfers a toner image to a sheet, is transferred to the sheet in the image forming device; and carrier developing in which magnetic carrier that has been mixed with toner is transferred to a sheet.