Multi-scale subfield alignment with anchor points
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Solution Overview
Problem
Current image alignment techniques, such as rigid and non-rigid alignments, fail to achieve high-precision sub-pixel-level alignment, especially when dealing with three-dimensional objects, leading to distortion and loss of structural information.
Innovation Solution
The proposed solution involves a multi-step process that includes global alignment, multi-scale subfield alignment, and anchor-point multi-scale-subfield alignment, where images are split into subfields, aligned using cross-correlation, and adjusted using anchor points and edge points to achieve precise pixel shifts and warping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rigid alignment is used, then alignment speed is maintained, but alignment precision deteriorates and structural information is lost
Solution Approach 1:
The patent divides the image into multiple subfields and performs alignment operations on each subfield separately. This segmentation allows for localized precision adjustments while maintaining overall image coherence, resolving the contradiction between achieving high alignment precision and managing process complexity.
Solution Approach 2:
The patent applies different alignment strategies to different regions of the image based on local characteristics. By identifying feature-rich regions and applying targeted alignment transformations only where needed, the method achieves high precision without uniformly increasing complexity across the entire image processing pipeline.
2Measurement precision
If multi-scale subfield alignment is used, then sub-pixel level alignment precision is improved, but computational complexity increases
Solution Approach 1:
The patent performs a preliminary global alignment step before conducting detailed subfield alignment. This preliminary action establishes a rough alignment framework that reduces the search space for subsequent precision alignment operations, thereby achieving sub-pixel precision without proportionally increasing computational power requirements.
Solution Approach 2:
The patent applies multi-scale alignment operations selectively to specific subfields rather than uniformly to the entire image. By focusing computational resources on regions that require precise alignment while using simpler methods for other areas, the method achieves high precision where needed without excessive overall computational complexity.
3Reliability
If anchor points are used for realignment, then structural information preservation is improved, but processing time increases
Solution Approach 1:
The patent extracts key anchor points and anchor-edge points from the image to serve as reference markers for alignment. By isolating these critical structural elements, the method preserves important structural information during alignment operations without needing to process every pixel, thereby reducing processing time while maintaining reliability.
Data Source
AI summary
Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.