Camera Lens Smudge Detection Using Adaptive Luminance Thresholds
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Solution Overview
Problem
Existing methods for detecting dirt, occlusion, and smudge on camera lenses and image sensors are influenced by ambient conditions and illumination variations, leading to detection errors.
Innovation Solution
A method involving pixel sampling, outlier exclusion, and threshold generation based on luminance averages and tolerances to adaptively detect dirt, occlusion, and smudge, independent of ambient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If edge detection based on brightness changes is used to detect dirt, occlusion and smudge, then the detection method is simple to implement, but the detection accuracy deteriorates due to influence from ambient conditions and illumination variations
Solution Approach 1:
The patent changes the detection parameter from simple brightness level to statistical parameters (mean and standard deviation) of pixel luminance values. By computing these statistical parameters and comparing them against dynamically determined thresholds, the system achieves more robust detection that is insensitive to ambient lighting conditions while maintaining computational simplicity.
Solution Approach 2:
The system performs self-calibration by automatically determining detection thresholds based on the statistical characteristics of the captured image itself. The threshold is derived from the mean and standard deviation of pixel luminance values in the image, allowing the detection algorithm to adapt to different lighting conditions without requiring external calibration or reference images.
2Productivity
If fixed threshold values are used for dirt detection, then the detection process is fast and simple, but the adaptability deteriorates when ambient conditions and illumination sources vary
Solution Approach 1:
The patent transforms the detection threshold from a fixed value to a dynamic value that adapts to different imaging conditions. The threshold is calculated as mean plus a multiple of standard deviation, where both mean and standard deviation are computed from the actual image data. This dynamic threshold automatically adjusts to different ambient conditions and illumination sources while maintaining detection speed.
3Measurement precision
If all pixels are processed for detection, then the detection precision is maximized, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by computing statistical parameters (mean and standard deviation) using only a subset of pixels or by using efficient algorithms that do not require processing every single pixel individually. This approach maintains detection precision by focusing computational effort on the most informative pixels while reducing overall computational complexity and processing time.
Data Source
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AI summary
A method of adaptably detecting dirt, occlusion and smudge on camera lens and image sensor includes capturing an image; subjecting original pixels of the image to sampling to result in sampled pixels; excluding outliers of the sampled pixels to result in retained pixels; obtaining an average value of the retained pixels; obtaining a tolerance value according to luminance values of the retained pixels; generating a threshold value for determining dirt, occlusion and smudge on camera lens and image sensor in the image according to the tolerance value and the average value of the retained pixels; and comparing a luminance value of at least one pixel of the image with a corresponding threshold value.