Camera Lens Contamination Detection via Histogram Analysis
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Solution Overview
Problem
When using image capturing terminals like smartphones or cameras, accidental finger contact can contaminate the camera surface, leading to fuzzy or blurry images due to unintentional contamination, especially when the user's fingers are stained, and the user may continue capturing images without realizing the contamination.
Innovation Solution
An image capturing method that extracts a target histogram feature from video frames to determine image quality and prompts the user to clean the camera by playing an audio file, flickering the flashlight, or vibrating the device when a fuzzy image quality is detected, ensuring clearer images are captured after cleaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the camera surface is used without cleaning, then the operation is convenient and quick, but the image quality deteriorates due to contamination
Solution Approach 1:
The system performs preliminary detection of camera lens contamination by analyzing video frames before the user captures an image. The histogram feature extraction and image quality recognition model evaluate the lens state in advance, allowing the system to prompt the user to clean the lens before contamination significantly degrades image quality. This preliminary action prevents the contradiction by addressing the issue before it affects the final outcome.
2Manufacturing precision
If image quality detection is performed, then the image quality improves, but the operation time and complexity increase
Solution Approach 1:
The system continuously extracts histogram features from video frames during the normal camera preview operation. Instead of performing separate detection steps that would interrupt the user workflow, the feature extraction and quality evaluation occur continuously in the background as the user normally uses the camera. This continuous action maintains image quality monitoring without adding noticeable time loss to the operation.
3Measurement precision
If histogram feature extraction is performed on video frames, then the detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The system extracts only the essential histogram features from video frames that are most indicative of lens contamination. By focusing on specific statistical properties of the image data (histogram distribution) rather than analyzing all image characteristics, the system achieves accurate contamination detection with reduced computational complexity. This selective extraction of key features resolves the contradiction between detection accuracy and computational burden.
Data Source
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AI summary
An image capturing method includes: obtaining a video frame during capturing images through a camera; extracting a target histogram feature based on the video frame; inputting the target histogram feature into an image quality recognition model which is used to determine whether an image is fuzzy based on a histogram feature, and outputting an image quality recognition result of the video frame; and in response to the image quality recognition result indicating that the video frame is fuzzy, prompting the user to clean the camera.