Image Blur Detection Using IMU Motion Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Images captured by cameras often suffer from blur due to camera motion during exposure, making them unusable for certain applications, and existing blur detection techniques are cumbersome, computationally intensive, and require straight edges in the image scene.
Innovation Solution
A system and method to determine a blur metric for images by using motion measurement data from inertial measurement units to create a camera rotation matrix and transformation matrix, which accounts for camera motion during exposure, allowing for real-time or offline quantification of blur by calculating pixel displacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion measurement data from IMU is used to create camera rotation matrix and transformation matrix for blur detection, then measurement precision and real-time capability are improved, but device complexity increases
Solution Approach 1:
The patent introduces an inertial measurement unit (IMU) as an intermediary device that measures camera motion independently of the image content. The IMU provides motion measurement data (angular velocities and accelerations) that serves as a mediator between the physical camera motion and the blur detection algorithm, enabling accurate blur metric calculation without requiring complex image analysis
Solution Approach 2:
The patent replaces traditional image-based blur detection methods with a physics-based approach using motion measurement data from the IMU. Instead of analyzing image features to detect blur, the system uses the camera rotation matrix and transformation matrix derived from IMU measurements to calculate pixel displacement and determine blur metrics, substituting mechanical/physical measurement with computational analysis
2Productivity
If traditional blur detection techniques are used, then computational complexity is reduced, but measurement precision and applicability to scenes without straight edges deteriorates
Solution Approach 1:
The patent enables the blur detection system to use the camera's own motion measurement data (from the IMU) to detect blur, making the system self-sufficient and independent of external scene features. The camera system serves itself by utilizing its inherent motion sensing capability rather than relying on scene-dependent image analysis methods
3Loss of time
If blur detection is performed in real-time during exposure, then productivity and response time are improved, but device complexity and computational load increase
Solution Approach 1:
The patent performs preliminary calculations by pre-computing the camera rotation matrix from IMU data and pre-calculating the transformation matrix that maps 3D scene points to 2D image coordinates. These preliminary computations enable real-time blur metric evaluation during or after exposure without requiring complex iterative processing, as the fundamental geometric relationships are established in advance
Data Source
AI summary
Among other things, one or more techniques and/or systems are provided for quantifying blur of an image. Blur may result due to motion of a camera while the image is captured. Accordingly, motion measurement data corresponding to motion of the camera during an exposure event may be used to create a camera rotation matrix. A camera intrinsic matrix may be obtained based upon a focal length and principle point of the camera. A transformation matrix may be estimated based upon the camera rotation matrix and/or the camera intrinsic matrix. The transformation matrix may be applied to pixels within the image to determine a blur metric for the image. In this way, blur of an image may be quantified offline and/or in real-time during operation of the camera (e.g., so that the image may be re-acquired (e.g., on the fly) if the image is regarded as being overly blurry).