Image Sensor Dirtiness Detection Using Fixed Pattern Analysis
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Solution Overview
Problem
Conventional auto clean machines lack a method to automatically detect and address the dirtiness of image sensors, which can lead to impaired tracking functionality over time, requiring frequent manual cleaning.
Innovation Solution
An electronic device with an image sensor that captures images at different time points or of a reference surface, calculates a fixed pattern difference to determine dirtiness levels, and generates a notification if the level exceeds a threshold, using multiple light sources and a control circuit to assess and respond to sensor cleanliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual cleaning of the image sensor is performed frequently, then the tracking function can be maintained, but the user convenience deteriorates and time is lost
Solution Approach 1:
The system performs self-diagnosis by automatically detecting image sensor dirtiness through image processing. The control circuit analyzes captured images to identify fixed patterns indicating dirt accumulation, eliminating the need for users to manually check or clean the sensor. This self-monitoring mechanism maintains tracking reliability while improving user convenience.
Solution Approach 2:
The system implements a feedback loop where the image sensor continuously captures images, the control circuit processes these images to detect dirtiness levels, and the system provides notifications when cleaning is needed. This closed-loop feedback mechanism ensures the tracking function remains reliable by proactively monitoring sensor condition without requiring user intervention.
2Ease of operation
If manual cleaning is performed only when the machine does not operate smoothly, then operational intervention is minimized, but the tracking function reliability deteriorates
Solution Approach 1:
The system performs preliminary detection of image sensor dirtiness by continuously analyzing captured images for fixed patterns. The control circuit identifies dirt accumulation before it significantly impacts tracking performance, enabling proactive maintenance. This preliminary action ensures tracking function reliability is maintained while minimizing the frequency and urgency of user interventions.
3Device complexity
If no dirtiness detection system is implemented, then the device complexity is reduced, but the tracking function reliability deteriorates due to undetected sensor dirtiness
Solution Approach 1:
The existing image sensor used for tracking purposes is dual-utilized for dirtiness detection as well. The control circuit performs additional image processing to identify fixed patterns indicative of dirt accumulation, extracting multiple functions from the same hardware components. This multi-functionality approach maintains tracking reliability without significantly increasing device complexity, as no separate detection hardware is required.
Data Source
AI summary
A dirtiness level determining method applied to an electronic device comprising an image sensor. The method comprises: (a) capturing a first image at a first time point according to first type of light; (b) capturing a second image at a second time point after the first time point according to the first type of light; (c) calculating a first fixed pattern according to a first difference between the first image and the second image; and (d) calculating a first dirtiness level of the image sensor according to the first fixed pattern; (e) generating a first notifying message if the first dirtiness level is higher than a dirtiness threshold.