Metadata-Driven Unwarping for Distorted Image Alignment
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Solution Overview
Problem
Conventional methods for aligning and unwarping images with excessive distortion, such as those taken with wide-angle or fisheye lenses, fail to produce high-quality results due to large distortions and inefficiencies, leading to poor alignment and stitching in panoramic image creation.
Innovation Solution
A metadata-driven multi-image processing method that uses camera/lens profiles to apply initial unwarping functions to feature points, estimate focal lengths and image centers, and perform global optimization to generate optimized unwarping functions and rotations, allowing for efficient alignment and unwarping of distorted images without the need for intermediate rectilinear images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional alignment methods are used on images with excessive distortion, then processing is simpler, but alignment quality and stitching results deteriorate
Solution Approach 1:
The patent applies preliminary unwarping to distorted images before alignment to remove excessive distortion. By pre-processing the images to correct lens distortions and create more regular geometric structures, the subsequent alignment operations can achieve high-quality results without requiring overly complex processing methods.
Solution Approach 2:
The patent segments the alignment process into distinct stages: initial unwarping, feature matching, and final refinement. This segmentation allows each stage to be optimized independently, managing complexity while maintaining high alignment quality through systematic multi-stage processing.
2Measurement precision
If intermediate rectilinear images are used in the workflow, then alignment accuracy improves, but computational and memory intensity increases
Solution Approach 1:
The patent extracts and removes the distortion component from images through unwarping operations, separating the distortion correction from the alignment process. This extraction allows alignment to proceed on corrected images without requiring full intermediate rectilinear transformations, reducing computational overhead while maintaining accuracy.
Solution Approach 2:
The patent applies partial unwarping corrections focused specifically on removing excessive distortion rather than performing complete rectilinear transformations. This partial action approach achieves sufficient alignment accuracy without the full computational cost of complete rectification, avoiding unnecessary processing intensity.
3Productivity
If lens distortion is not corrected, then processing speed is faster, but panoramic stitching quality deteriorates
Solution Approach 1:
The patent performs preliminary unwarping to correct lens distortion before the alignment and stitching stages. By addressing distortion early in the pipeline, the subsequent processing can proceed efficiently without repeatedly handling distorted geometries, maintaining both speed and quality.
Solution Approach 2:
The patent changes the geometric parameters of distorted images through unwarping transformations, converting them to a more regular coordinate system. This parameter transformation enables standard alignment algorithms to work effectively, improving stitching quality without requiring specialized slow processing for distorted images.
Data Source
AI summary
Methods and apparatus for processing collections of images are described in which metadata from a set of images may be used in directing a multi-image processing workflow. One or more output images may be rendered from a set of input images, with each output image being some combination of two or more of the input images. To render an output image, a workflow including one or more processing steps may be applied to the images. Metadata may be used in directing and performing the workflow. For example, metadata may be used in determining a particular workflow for a set of images. As another example, metadata may be used to sort a collection of images into multiple categories for automated workflow processing. As yet another example, metadata may be used to retrieve information stored in a profile database that may be used in processing the images.