AI Segmentation Registration for Multi-Frame Foreground-Background Alignment
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Solution Overview
Problem
Multi-frame registration in image processing systems often results in sub-optimal outcomes due to inaccuracies, leading to blurry foreground or background images when foreground and background features dominate, affecting the quality of the final blended image.
Innovation Solution
Implement AI segmentation-based registration that separates foreground and background in each frame, aligns them separately, and combines the registered frames to generate a combined registered multi-frame image, improving the realism of the final output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multi-frame registration is performed using homography to align all frames together, then the processing is simple and fast, but the registration accuracy deteriorates when foreground and background features dominate, leading to blurry images
Solution Approach 1:
The patent divides the image into foreground and background segments using segmentation masks. Separate homography matrices are computed for foreground features and background features independently, allowing each segment to be registered with its own optimal transformation. This resolves the contradiction by improving registration accuracy through segmentation while managing complexity through automated mask generation and separate processing pipelines.
2Measurement precision
If separate registration is performed for foreground and background frames, then the registration accuracy is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs segmentation mask generation as a preliminary step before registration. By pre-separating foreground and background regions, the subsequent registration process can proceed in parallel for both segments, reducing overall processing time. The segmentation is performed once on the reference frame, and the resulting masks are applied to all other frames, avoiding repeated segmentation operations.
Solution Approach 2:
The patent implements a dynamic processing pipeline where the complexity of separate foreground-background registration is adaptively managed. The system uses automated segmentation algorithms that can adjust to different scene compositions, and the separate registration operations are performed efficiently using optimized homography computation. This dynamic approach maintains high accuracy while minimizing processing time through intelligent resource allocation.
Data Source
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.