Predictive Hand Tremor Compensation for Image Stabilization
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Solution Overview
Problem
Conventional image stabilization methods, such as feedback stabilization systems that use actuators and post-processing systems, are costly and power-intensive, and face challenges with increased processing complexity as image resolution improves, while also failing to effectively address human hand motion-induced blur in photography.
Innovation Solution
A method utilizing motion data from accelerometers or gyroscopes, combined with biomechanical knowledge, to predict and compensate for hand tremors by analyzing acceleration vectors, allowing for predictive image capture and reduced post-processing complexity, eliminating the need for actuators and scaling well with image size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feedback stabilization systems use actuators to adjust sensor and lens position, then image stabilization is achieved, but cost and power consumption increase significantly
Solution Approach 1:
The patent replaces the mechanical actuator-based feedback stabilization system with a computational approach. Motion data from sensors is processed through algorithms that predict hand tremor patterns and compensate for blur in the captured images, eliminating the need for mechanical actuators while achieving similar stabilization效果
Solution Approach 2:
The patent creates a computational model of hand tremor motion based on sensor data and biomechanical principles. This model copies the essential characteristics of hand motion to predict and compensate for blur, replacing the need for physical actuation systems
2Reliability
If post-processing systems analyze captured images to infer motion degree, then blur compensation is achieved, but processing complexity and time increase significantly
Solution Approach 1:
The patent performs motion analysis and tremor pattern recognition before the actual image capture. By predicting the hand motion trajectory in advance using sensor data and biomechanical models, the system prepares compensation parameters ahead of time, reducing the processing burden during post-processing
Solution Approach 2:
The patent introduces motion sensors and intermediate computational models as mediators between the hand motion and the image capture process. These intermediaries provide direct motion measurements and predictive models that simplify the post-processing analysis compared to analyzing only the final image data
3Measurement precision
If image resolution is increased to improve photo quality, then image detail is improved, but processing complexity increases
Solution Approach 1:
The patent performs motion prediction and compensation parameter calculation before high-resolution image capture. By preparing the compensation model in advance based on sensor data, the system avoids the need to process and analyze high-resolution image data to determine motion, thus maintaining low processing complexity while capturing high-detail images
Data Source
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AI summary
A method and apparatus for receiving motion data and determining acceleration vectors based on biomechanical data and the received motion data. The method further comprises creating an improved image based on the acceleration vectors.