Optical Image Striping Anomaly Detection Using Intensity Gradient Analysis
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Solution Overview
Problem
Optical scanning devices often produce images with striping anomalies due to defective polygonal mirror facets, leading to high rejection rates and costs, with existing solutions relying on human observation or inadequate automatic detection methods.
Innovation Solution
A method involving the determination of intensity gradients between optical image pixels, followed by a modulo-n averaging filter to identify and correct anomalies, allowing for automated detection and correction of striping without the need for test images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated line noise detection is implemented, then productivity is improved, but measurement precision deteriorates because existing automatic methods are inadequate for detecting striping anomalies
Solution Approach 1:
The patent replaces the manual visual inspection mechanism with an automated computational mechanism. The system uses a processor to execute algorithms that calculate intensity gradients between adjacent pixels and apply modulo-n averaging filters to detect striping anomalies, substituting human observation with automated image processing techniques.
Solution Approach 2:
The patent transforms the detection approach by changing the parameter being analyzed from raw pixel intensity to intensity gradient magnitude. By calculating the gradient between adjacent pixels and applying thresholding, the system enhances the detectability of striping anomalies while maintaining automated operation.
2Reliability
If strict quality control is applied to reject images with striping, then reliability is improved, but productivity deteriorates due to high rejection rates
Solution Approach 1:
The patent applies local quality assessment by evaluating intensity gradients at specific pixel locations rather than rejecting entire images based on global criteria. The modulo-n averaging filter identifies localized striping patterns, allowing the system to distinguish between acceptable variations and actual defects, thereby reducing unnecessary rejections.
Solution Approach 2:
The system implements feedback by automatically analyzing detected anomalies and providing rejection decisions without manual intervention. The automated detection loop processes images, identifies striping patterns through gradient analysis, and applies consistent quality criteria, eliminating human subjectivity and improving decision consistency.
3Measurement precision
If manual observation is used for striping detection, then measurement precision is improved, but labor costs increase and productivity decreases
Solution Approach 1:
The patent replaces the manual visual inspection mechanism with an automated computational mechanism. The system uses a processor to execute algorithms that calculate intensity gradients between adjacent pixels and apply modulo-n averaging filters to detect striping anomalies, substituting human observation with automated image processing techniques.
Solution Approach 2:
The system performs self-service by automatically detecting and identifying striping anomalies without requiring human operators. The automated algorithm processes images, calculates gradients, applies filtering, and makes rejection decisions independently, eliminating the need for manual labor while maintaining detection capability.
Data Source
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AI summary
An embodiment of the invention provides an apparatus and a method. An intensity gradient between first and second optical image data of an optical image produced by an optical scanning device is determined. The first optical image data is a first optical image pixel and the second optical image data is a second optical image pixel. An intensity gradient image comprising a plurality of intensity gradient image pixels is determined, each intensity gradient image pixel representing the magnitude of an intensity gradient. One or more anomalies in the optical image in dependence on the magnitude of the intensity gradient being greater than or equal to a threshold value is identified.