Parametric Deformation Model for Multi-Modal Image Co-Registration
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Solution Overview
Problem
Existing image registration systems face challenges in efficiently and accurately aligning images captured by different sensors, such as infrared and visible sensors, due to varying capture parameters like camera locations and time, which complicates real-time processing and analysis in applications like unmanned aerial vehicles and video surveillance.
Innovation Solution
The proposed solution involves a parametric deformation model with 4 degrees of freedom (translation, rotation, scaling) to align infrared and visible images by minimizing a loss function based on directional gradients, using a Nelder-Mead search or other numerical methods, and incorporating a penalty term to maintain deformation parameters within reasonable bounds, allowing for efficient and flexible image registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image registration methods are used to align images from different sensors, then alignment accuracy can be improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The patent transforms the image registration problem from pixel-space matching to parameter-space optimization by defining a parametric deformation model with 4 degrees of freedom (translation, rotation, scaling). This parameter transformation enables efficient optimization using numerical methods like Nelder-Mead, achieving accurate alignment without exhaustive pixel-level comparison, thus resolving the contradiction between alignment accuracy and processing time.
Solution Approach 2:
The patent replaces traditional mechanical image registration approaches (manual alignment, feature-point matching) with a mathematical optimization system. By formulating registration as a loss function minimization problem and using gradient-based numerical optimization, the system achieves automated, efficient, and accurate image alignment, substituting complex mechanical processing with streamlined mathematical computation.
2Adaptability or versatility
If images with different capture parameters are registered, then multi-modal analysis capability is improved, but registration difficulty increases
Solution Approach 1:
The patent creates a universal parametric deformation model that can handle multiple types of image pairs (infrared-visible, stereo, temporal) through a unified mathematical framework. The 4-degree-of-freedom transformation model and loss function formulation are general enough to accommodate different sensor types and capture conditions, enabling multi-modal analysis while maintaining consistent registration methodology, thus resolving the contradiction between versatility and complexity.
3Productivity
If real-time processing is implemented, then system responsiveness is improved, but registration accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-defining the parametric deformation model structure and loss function before actual registration occurs. This preparation enables rapid online processing where only the optimization step is needed, achieving real-time performance. The pre-established mathematical framework allows the system to quickly compute accurate alignments without extensive processing, resolving the contradiction between real-time capability and accuracy.
Data Source
AI summary
Image pair co-registration systems and methods include receiving a pair of multi-modal images, defining a parametric deformation model, defining a loss function that is minimized when the pair of images are aligned, and performing a multi-scale search to determine deformation parameters that minimize the loss function. The optimized deformation parameters define an alignment of the pair of images. The pair of images may include visible spectrum image and an infrared image. The method further includes resizing the visible spectrum image to match the infrared image, applying at least one lens distortion correction model, and normalizing a dynamic range of each of the pair of images. The multi-scale search may further include resizing the pair of images to a current processing scale, applying adaptive histogram equalization to the pair of images to generate equalized images, applying Gaussian Blur to the equalized images, and optimizing the deformation parameters.