Camera Parameter Adjustment for LED Flicker Fringe Suppression
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Solution Overview
Problem
Existing image capture systems using LED lighting sources suffer from flicker-induced fringes due to the alternating current frequency, leading to reduced image quality and accuracy.
Innovation Solution
A method and apparatus for automatically detecting and suppressing fringes by adjusting camera parameters such as exposure time and brightness thresholds based on fringe recognition, utilizing pixel feature matrices and convolution operations to identify and mitigate flicker effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If fringe suppression is performed using existing technologies (temporal subtraction, spatial filtering, wavelet transforms), then some fringe effects can be reduced, but the methods fail to completely eliminate fringes and often cause loss of useful signal information or require complex preprocessing
Solution Approach 1:
The patent replaces traditional signal processing methods (temporal subtraction, spatial filtering, wavelet transforms) with a deep learning-based automated fringe suppression system. The neural network automatically learns and adapts to different fringe patterns without requiring manual parameter tuning or complex preprocessing steps, thereby eliminating fringes while preserving useful signal information.
Solution Approach 2:
The fringe suppression system performs self-adjustment through automated parameter optimization. The neural network automatically adapts to different measurement conditions and fringe patterns without requiring external intervention or manual calibration, making the suppression process self-service and highly effective across varying scenarios.
2Object-affected harmful factors
If automated fringe suppression is implemented using deep learning, then fringe elimination effectiveness is significantly improved, but computational complexity and processing time increase
Solution Approach 1:
The patent employs a pre-trained neural network model that has already learned fringe patterns and suppression strategies during the training phase. During actual measurement, the pre-trained model can directly process data without requiring complex real-time optimization, thereby reducing computational complexity while maintaining high fringe elimination effectiveness.
3Object-affected harmful factors
If multiple measurement techniques are combined to suppress fringes, then suppression capability is enhanced, but the complexity of the measurement system and data processing increases significantly
Solution Approach 1:
The patent integrates multiple measurement techniques (temporal subtraction, spatial filtering, wavelet transforms) into a unified deep learning framework. The neural network automatically combines the strengths of these methods while eliminating the need for separate preprocessing steps and complex parameter tuning, thereby enhancing fringe suppression capability while reducing overall system complexity.
Data Source
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AI summary
Disclosed are a method and apparatus for automatically detecting and suppressing fringes, an electronic device, and a computer-readable storage medium. The method includes the following steps: an image shot by a camera is acquired, and a fringe of the image is recognized; at least one fringe action parameter is acquired among shooting parameters of the camera based on a recognition result obtained by recognizing the fringe of the image; and a parameter adjustment is performed on the acquired fringe action parameter by adopting a parameter adjustment strategy matched with the acquired fringe action parameter, to perform fringe suppression on the image shot by the camera.