Point Source Image Blur Mitigation via Trajectory Estimation
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Solution Overview
Problem
Microbolometer detectors suffer from significant blurring when capturing images of moving point sources due to relative motion, which hinders target tracking and intensity measurement in both commercial and military applications.
Innovation Solution
An apparatus and method that digitally mitigates image blur by determining the trajectory of a point source using an array of sensors and processing circuitry, employing a statistical maximum likelihood approach to estimate the point source's location at a sub-pixel scale, and leveraging inertial measurement unit data to refine motion calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If microbolometer detectors are used to capture infrared imagery, then cost is reduced significantly, but image clarity deteriorates due to significant blurring when there is relative motion between target and detector
Solution Approach 1:
The system performs preliminary actions by capturing multiple frames at different time instances and predicting the trajectory of the point source before the blurring fully occurs. This allows the system to compensate for motion effects in advance rather than attempting to correct fully blurred images, thereby maintaining measurement precision while using cost-effective microbolometer detectors
Solution Approach 2:
The system employs feedback mechanisms by using inertial measurement unit (IMU) data to continuously update the predicted trajectory of moving targets. This feedback loop allows real-time adjustment of trajectory predictions based on actual motion data, improving image clarity through dynamic compensation while maintaining the use of inexpensive microbolometer detectors
2Measurement precision
If the stare time of the imaging detector is increased to improve signal capture, then measurement precision improves, but blurring worsens due to relative motion during the extended capture period
Solution Approach 1:
The system performs preliminary trajectory prediction using IMU data and motion models before completing the full stare time capture. By predicting where the target will be at different time instances, the system can allocate signal integration across multiple frames while maintaining image sharpness through computational correction, thus achieving both improved signal capture and maintained image quality
Solution Approach 2:
The system transitions from a single-time-point image capture to a multi-dimensional approach by capturing signals across multiple time instances and spatial locations. By adding the time dimension and using trajectory prediction, the system can integrate signals over extended periods while compensating for motion, thereby improving signal capture accuracy without sacrificing image sharpness
3Measurement precision
If digital de-blurring processing is applied to correct motion blur, then image clarity improves, but processing complexity increases
Solution Approach 1:
The system extracts only the essential motion information from IMU data and pixel signals to perform de-blurring, rather than processing the entire image data set. By isolating and processing only the trajectory-related parameters, the system achieves effective image clarity improvement while minimizing processing complexity
Solution Approach 2:
The system replaces complex mechanical or optical de-blurring mechanisms with a computational approach using maximum likelihood estimation. This substitution uses statistical methods to infer the most probable target trajectory and location, achieving image clarity improvement through algorithmic processing rather than complex hardware systems
4Measurement precision
If trajectory prediction using maximum likelihood estimation is implemented, then target location accuracy improves, but computational requirements increase
Solution Approach 1:
The system performs preliminary computations by pre-calculating trajectory predictions based on IMU data before processing the actual pixel signals. This preliminary action reduces the computational burden during the main processing stage, as the maximum likelihood estimation can be performed more efficiently with pre-prepared motion parameters, thereby improving target location accuracy while reducing computational energy requirements
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
This solution effectively de-blurs images in near-real-time, enabling precise tracking of point sources and enhancing the application scope of microbolometer detectors in navigation and tactical systems by improving the accuracy of target location and amplitude estimation.
Implementation Method 1
an imaging detector including an array of sensors. The imaging detector may be configured to capture radiant energy corresponding to an image over a stare time and generate a plurality of pixel signals associated with respective pixels of the image
Data Source
AI summary
Apparatuses and methods for point source image blur mitigation are provided. An example method may include receiving, from an imaging detector, a plurality of pixel signals associated with respective pixels of an image over a stare time, and determining a trajectory of a point source within the image due to relative angular motion of the point source across a plurality of pixels of the image. The example method may further include determining a subset of pixels that intersect with the trajectory, and determining an estimated location of the point source within the image at an end of the stare time based on the pixel signals for each of the pixels within the subset of pixels.