Image Stabilization Apparatus Adaptive Weighting Parallel Shake
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Solution Overview
Problem
Current image stabilization systems face challenges in accurately compensating for parallel shake, particularly at close ranges or with high focal lengths, due to noise interference from accelerometers and low correlation between gyroscope and accelerometer signals, leading to overcompensation and deterioration in image stabilization performance.
Innovation Solution
An image stabilization apparatus that includes multiple shake detection units, an orientation detection unit, and a calculation unit to weight compensation values based on the correlation between signals from different detection methods, ensuring accurate compensation for both angular and parallel shake across various camera orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If accelerometer output is used to detect parallel shake, then parallel shake detection capability is improved, but measurement precision deteriorates due to disturbance noise and environmental changes
Solution Approach 1:
The patent introduces an intermediary processing mechanism that mediates between the noisy accelerometer output and the final parallel shake detection result. By using multiple accelerometers arranged in specific orientations and processing their outputs through correlation analysis, the system filters out disturbance noise and environmental changes while preserving the actual parallel shake signal.
Solution Approach 2:
The patent changes the parameter of accelerometer arrangement by using multiple accelerometers with different sensing orientations (e.g., X-axis and Y-axis accelerometers). This parameter change allows the system to detect parallel shake in multiple directions and use correlation analysis to distinguish true shake from noise, thereby improving measurement precision while maintaining detection capability.
2Adaptability or versatility
If rotation radius is computed using gyroscope and accelerometer, then parallel shake compensation is enabled, but measurement precision deteriorates when sensor noise significantly influences the computation
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors the correlation between gyroscope and accelerometer signals. When the correlation indicates low signal quality (high noise influence), the system adjusts the rotation radius computation by relying more on accelerometer data and less on gyroscope data, thereby maintaining measurement precision while preserving compensation capability.
Solution Approach 2:
The patent makes the rotation radius computation dynamic by adaptively weighting the contributions of gyroscope and accelerometer data based on real-time signal correlation. This dynamic adjustment allows the system to optimize measurement precision under varying noise conditions while maintaining the ability to compensate for parallel shake across different operating scenarios.
3Measurement precision
If parallel shake compensation is increased for high acceleration output, then compensation accuracy is improved, but device complexity increases due to adaptive weighting mechanisms
Solution Approach 1:
The patent applies partial action by selectively activating adaptive weighting only when signal correlation indicates the need for it. Instead of continuously applying complex adaptive algorithms, the system uses simpler computation when noise influence is low, thereby reducing device complexity while maintaining high compensation accuracy when it is most needed.
4Object-affected harmful factors
If gyroscope and accelerometer correlation is low, then sensor noise influence is reduced, but measurement precision deteriorates due to misdetected parallel shake
Solution Approach 1:
The patent introduces correlation analysis as an intermediary mechanism that evaluates the quality of the relationship between gyroscope and accelerometer signals. When correlation is low, the system interprets this as high noise influence and adjusts its detection strategy accordingly, using alternative computation methods that maintain measurement precision despite the low correlation between sensors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides high-accuracy parallel shake compensation, preventing overcompensation and improving image stabilization performance by adaptively adjusting compensation values based on orientation and signal correlation, thereby enhancing image clarity.
Implementation Method 1
a first shake detection unit (108p, 108y) that detects angular shake
Implementation Method 2
a second shake detection unit (109p, 109y) that detects parallel shake
Implementation Method 3
an image stabilization unit (110) that performs image blurring compensation by displacing a compensation lens (111) in directions to drive axes that are orthogonal to an optical axis (102)
Data Source
AI summary
An image stabilization apparatus includes an image stabilization unit that compensates image blurring by moving a compensation member, a first shake detection unit that detects an angular velocity of the shake, a second shake detection unit that detects shake by a different method, an orientation detection unit that detects an inclination status of an optical apparatus, a calculation unit that calculates a compensation value from signals of the first and second shake detection units, and an output unit that compensates the output of the first shake detection unit using the compensation value, with the calculation unit performing weighting on the compensation value of a drive axis of an image stabilization mechanism, based on a correlation between the first signal and the second signal.