Camera Lens and Image Sensor Contamination Detection Using Adaptive Thresholds
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Solution Overview
Problem
Existing methods for detecting dirt, occlusion, and smudge on camera lenses and image sensors are prone to errors due to variations in ambient conditions and illumination, leading to impaired camera performance.
Innovation Solution
A method involving pixel sampling, outlier exclusion, and threshold value generation based on luminance values to adaptively detect dirt, occlusion, and smudge, using a digital image processor to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge detection based on brightness level changes is used to detect dirt, occlusion and smudge, then the detection process can be implemented, but the detection accuracy deteriorates due to influence from ambient conditions and illumination source variations
Solution Approach 1:
The patent transforms the detection approach by changing from direct brightness level analysis to analyzing the distribution characteristics of pixel luminance values. By computing statistical parameters (mean, standard deviation) of pixel luminance distributions and comparing them against threshold values, the system achieves robust detection that is insensitive to ambient lighting variations. This parameter transformation resolves the contradiction by making detection accuracy independent of illumination conditions.
2Reliability
If traditional edge detection methods are used, then the implementation is straightforward, but the reliability deteriorates due to errors from ambient condition variations
Solution Approach 1:
The patent replaces the traditional edge detection mechanism (which relies on gradient-based brightness changes) with a statistical analysis mechanism. Instead of detecting edges through mechanical differentiation of brightness values, the system uses statistical distribution analysis of pixel luminance values. This substitution improves reliability by replacing a fragile edge-detection mechanism with a robust statistical comparison approach that accounts for ambient variations.
3Adaptability or versatility
If fixed threshold values are used for detection, then the algorithm is simple, but the adaptability deteriorates when lighting conditions vary
Solution Approach 1:
The patent applies preliminary action by pre-establishing a database of threshold values corresponding to different lighting conditions and camera parameters. Before actual detection, the system determines the current lighting condition category and selects the appropriate pre-computed threshold values. This preliminary preparation eliminates the need for complex real-time threshold optimization, achieving both adaptability to varying lighting conditions and computational efficiency through pre-processing.
Data Source
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.