AI Segmentation Registration for Sharp Multi-Frame Imaging
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Solution Overview
Problem
Conventional multi-frame registration techniques often prioritize either the foreground or background features, leading to sub-optimal results with blurry foreground or background in the final blended image due to inaccurate alignment.
Innovation Solution
Implement AI segmentation-based registration to separate foreground and background in each frame, align them separately, and combine the registered frames to generate a more realistic output image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional multi-frame registration techniques prioritize either foreground or background features, then alignment speed is improved, but image quality deteriorates due to blurry foreground or background
Solution Approach 1:
The patent applies segmentation by dividing the image into foreground and background regions using a segmentation model. This allows separate registration processes to be applied to each region, with the foreground using feature point matching and the background using homography transformation. This resolves the contradiction by enabling both regions to be optimized simultaneously rather than prioritizing one over the other.
Solution Approach 2:
The patent implements local quality by applying different registration strategies to different parts of the image. The foreground region uses a registration method optimized for moving objects (feature point matching), while the background region uses a method optimized for static scenes (homography). This local differentiation ensures both regions achieve high alignment quality without compromising the other.
2Manufacturing precision
If AI segmentation-based registration is implemented to separate and align foreground and background separately, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent uses AI-based segmentation to divide the image into foreground and background regions, enabling separate registration processing for each. This segmentation approach improves alignment accuracy by applying region-specific registration methods while managing complexity through parallel processing of the segmented regions.
Solution Approach 2:
The patent replaces traditional mechanical or manual registration methods with AI-based segmentation and automated registration algorithms. This substitution improves alignment accuracy through intelligent region classification and automated parameter optimization, while the computational nature of the system manages complexity more efficiently than manual methods.
3Speed
If traditional homography-based registration is used for all frames, then processing speed is maintained, but registration accuracy deteriorates for scenes with both foreground and background movement
Solution Approach 1:
The patent segments the image into foreground and background regions to apply different registration methods. The background uses efficient homography transformation for speed, while the foreground uses more accurate feature point matching. This segmentation enables both speed and accuracy to be optimized in their respective regions simultaneously.
Solution Approach 2:
The patent applies local quality by using different registration algorithms for different regions based on their motion characteristics. Static background regions use homography for speed, while moving foreground regions use feature point matching for accuracy. This local differentiation resolves the contradiction by matching the registration method to the local requirements of each region.
Data Source
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AI summary
A method includes obtaining a reference frame from among multiple image frames of a scene. The method also includes generating a segmentation mask using the reference frame, where the segmentation mask contains information for separation of foreground and background in the scene. The method further includes applying the segmentation mask to each of the multiple image frames to generate foreground image frames and background image frames. The method also includes performing multi-frame registration on each of the foreground image frames to generate registered foreground image frames. The method further includes performing multi-frame registration on each of the background image frames to generate registered background image frames. In addition, the method includes combining the registered foreground image frames and the registered background image frames to generate a combined registered multi-frame image of the scene.