Gradient Domain Image Registration via Energy Minimization
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Solution Overview
Problem
Existing image registration methods struggle with robust alignment of images with severe spatially-varying intensity distortions, leading to incorrect alignment and high computational complexity, especially in applications like medical imaging and biometrics where intensity artifacts are prevalent.
Innovation Solution
The method employs an energy minimization function based on low-rank approximation and sparsity in the gradient domain, using an augmented Lagrange algorithm to increment registration parameters and minimize gradient errors, ensuring accurate alignment even under intense intensity variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intensity-based distance measures are used for image registration, then registration can be performed, but the registration fails to be robust to severe intensity distortions and spatially-varying intensity variations
Solution Approach 1:
The patent introduces gradient images as an intermediary representation between the original intensity images and the registration process. By working in the gradient domain rather than directly with intensity values, the method mediates the effect of intensity distortions, allowing registration to proceed robustly despite severe intensity variations and spatially-varying intensity artifacts.
Solution Approach 2:
The patent transforms the registration problem from the intensity domain to the gradient domain by changing the fundamental parameter used for similarity measurement. Instead of using intensity values directly, the method uses gradient information (derivatives of intensity), which fundamentally changes how similarity is assessed and makes the registration robust to intensity distortions while maintaining alignment accuracy.
2Productivity
If existing intensity-based registration methods are used, then registration can be performed, but computational complexity increases and multiple local minima are encountered
Solution Approach 1:
The patent replaces the complex iterative optimization process of intensity-based registration with a more direct gradient-based approach. By substituting the mechanical system of intensity similarity measurement with gradient domain processing, the method achieves registration while reducing computational complexity and avoiding the trap of multiple local minima that plagues intensity-based methods.
3Reliability
If sparsity-inducing similarity measures are used, then registration can handle sparse errors, but the methods fail when images contain severe spatially-varying intensity distortions that are not sparse
Solution Approach 1:
The patent moves the registration analysis from the intensity domain to the gradient domain, effectively adding a new dimension to the problem. This dimensional change allows the method to handle both sparse errors and non-sparse spatially-varying intensity distortions uniformly, as gradient information captures structural relationships that remain stable despite intensity variations of any type or distribution.
Data Source
AI summary
A method, apparatus and computer program product are provided for image registration in the gradient domain. A method is provided including receiving three or more input images and registering, simultaneously, the three or more input images in the gradient domain based on applying an energy minimization function.