Image Processing Noise Point Identification for Sheet Defects
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Solution Overview
Problem
Existing image processing methods struggle to accurately determine whether a noise point in an image is a sheet noise or an image defect, leading to incorrect identification and subsequent inappropriate corrective measures in image forming apparatuses like printers.
Innovation Solution
An image processing method and apparatus that select a target sheet based on input information, derive feature information from a noise point in the image, and apply a determination algorithm to distinguish between sheet noise and image defects, using a combination of feature extraction and pattern recognition techniques to identify the cause of noise points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single determination algorithm is used for all sheet types, then the device complexity is reduced, but the measurement precision for identifying sheet noise decreases
Solution Approach 1:
The patent segments the determination process into two distinct algorithms: a first determination algorithm for determining sheet noise on transparent sheets, and a second determination algorithm for determining sheet noise on non-transparent sheets. This segmentation allows each algorithm to be optimized for its specific sheet type, improving measurement precision while managing device complexity through modular design
Solution Approach 2:
The patent implements dynamic selection of determination algorithms based on the transparency characteristics of the sheet being processed. The system automatically switches between the first and second determination algorithms according to the sheet type, enabling adaptive optimization of noise point identification accuracy without requiring a single complex algorithm to handle all cases
2Measurement precision
If feature information is derived from multiple image attributes, then the measurement precision improves, but the loss of information increases
Solution Approach 1:
The patent extracts specific feature information from the target image including color characteristics, density values, and positional coordinates of noise points. By selectively extracting only the most relevant features needed for sheet noise determination, the system improves measurement precision while minimizing information loss by excluding redundant data
Solution Approach 2:
The patent applies different feature extraction strategies based on the local characteristics of the sheet being processed. For transparent sheets, specific color and density features are extracted using the first determination algorithm, while non-transparent sheets use the second determination algorithm with tailored feature extraction, ensuring optimal information utilization for each sheet type
Data Source
AI summary
A processor selects a target sheet from a plurality of predetermined sheet candidates in accordance with selection information that is input via an input device. Furthermore, the processor derives feature information regarding a noise point from a target image that is obtained through an image reading process performed on an output sheet output from an image forming device, the noise point being a dot-like noise image included in the target image. Furthermore, the processor determines whether or not the noise point is a dot-like sheet noise by applying the feature information to a determination algorithm that corresponds to the target sheet, the sheet noise being included in a sheet of the output sheet itself, the determination algorithm being one of a plurality of determination algorithms that respectively correspond to the plurality of sheet candidates.