Image Reading Apparatus Dust Detection Noise Suppression
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Solution Overview
Problem
Conventional image reading apparatuses face inefficiencies in dust detection due to long startup times, reduced reading efficiency, and decreased accuracy caused by random noise components in image data, leading to erroneous detection of dust and scratches.
Innovation Solution
The implementation of a random noise suppressing device that averages or interpolates image data, combined with a shading correction device, to enhance the accuracy of dust detection by suppressing random noise components in the image data before anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional dust detection is performed using raw image data, then detection speed is maintained, but detection accuracy deteriorates due to random noise components causing erroneous false positives
Solution Approach 1:
The patent applies preliminary action by performing noise suppression processing on image data before dust detection. The random noise suppressing device processes the image data in advance to remove noise components, ensuring that subsequent dust detection operates on cleaned data, thereby preventing false positives while maintaining detection speed
Solution Approach 2:
The patent introduces an intermediary element - the random noise suppressing device - that acts as a mediator between image data acquisition and dust detection. This intermediary processes the raw image data to eliminate noise components before the detection phase, improving both accuracy and reliability without creating a direct trade-off
2Measurement precision
If noise suppression processing is applied to image data before dust detection, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent applies parameter changes by optimizing the noise suppression algorithm to operate with specific parameters that balance processing speed and effectiveness. By adjusting parameters such as the degree of smoothing or the threshold for noise removal, the system achieves adequate noise suppression without excessive processing time, maintaining real-time detection capability
Solution Approach 2:
The patent implements skipping by using efficient noise suppression techniques that quickly process image data without thorough multi-stage filtering. The system rushes through the noise suppression process using optimized algorithms that provide sufficient noise reduction in a single pass, preventing time loss while improving detection accuracy
3Measurement precision
If standard white plate is used for shading correction, then correction accuracy is maintained, but the plate itself becomes susceptible to dust contamination affecting future readings
Solution Approach 1:
The patent applies preliminary action by implementing dust detection on the standard white plate before it is used for shading correction. By detecting and accounting for dust particles in advance, the system can compensate for their presence during correction operations, maintaining accuracy while preventing dust from degrading future measurement quality
Solution Approach 2:
The patent implements feedback by using dust detection results to adjust the shading correction process. The system detects dust on the reference plate, feeds this information back into the correction algorithm, and modifies the correction accordingly, thereby maintaining correction accuracy despite the presence of contamination on the reference plate
Data Source
AI summary
There is provided an image reading apparatus that is capable increasing the detection accuracy for dust, scratches, dirt, and the like. Sixty-four lines of sampling points on a reference standard white plate are read, shading correction and averaging are carried out the read image data to suppress random noise components in the image data, the data is then stored in a sampling memory, and dust detection is carried out on the data stored in the sampling memory. Alternatively, shading correction and dust detection are carried out on image data that has been subjected to linear interpolation. It is thus possible to increase the accuracy of detection of dust, scratches, dirt, and the like while ensuring that shading correction is performed at high speed.