Extended Range Image Processing via Motion Vector Registration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electro-optical (EO) systems face challenges in processing images of moving targets and scenes with high noise and limited resolution, especially at long ranges, due to issues with motion tracking and integration of image frames.
Innovation Solution
The proposed solution involves generating motion vectors by computing correlation functions between reference and current frames at registration points, registering the current frame based on these vectors, and integrating frames over time to produce stabilized and noise-reduced images, enhancing both scene and target tracking capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion vectors are generated by computing correlation functions at multiple registration points to improve tracking accuracy, then target detection precision is improved, but computational complexity increases
Solution Approach 1:
The patent divides the image frame into multiple registration points (N registration points) and computes correlation functions independently at each point. This segmentation allows parallel processing of different regions, improving target detection precision through comprehensive coverage while managing computational complexity through distributed calculation across multiple points rather than processing the entire frame as a single unit.
2Reliability
If multiple image frames are integrated over time to reduce noise and improve resolution, then signal-to-noise ratio is improved, but processing time increases
Solution Approach 1:
The patent implements continuous frame integration where multiple image frames are processed and combined over time to produce stabilized output. By maintaining continuous processing of incoming frames and integrating them systematically, the system reduces noise and improves signal-to-noise ratio while managing processing time through ongoing rather than batch processing, allowing real-time or near-real-time output generation.
3Speed
If image frames are registered and integrated in real-time for continuous imaging, then imaging speed is improved, but noise reduction capability deteriorates
Solution Approach 1:
The patent applies partial integration by selecting and integrating a subset of frames rather than all available frames, or by integrating frames with different weighting factors. This partial action approach maintains real-time imaging speed by not waiting for complete frame sequences while still achieving sufficient noise reduction through selective integration of the most relevant frames, balancing speed and noise reduction capability.
Data Source
AI summary
Methods and systems for image processing are provided. A method for processing images of a scene includes receiving image data of a reference and a current frame; generating N motion vectors that describe motion of the image data within the scene by computing a correlation function on the reference and current frames at each of N registration points; registering the current frame based on the N motion vectors to produce a registered current frame; and updating the image data of the scene based on the registered current frame. Optionally, registered frames may be oversampled. Techniques for generating the N motion vectors according to roll, zoom, shift and optical flow calculations, updating image data of the scene according to switched and intermediate integration approaches, re-introducing smoothed motion into image data of the scene, re-initializing the process, and processing images of a scene and moving target within the scene are provided.


