Multi-Lens Distortion Simulation via Segmented Pixel Shifting
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Solution Overview
Problem
Existing image simulation methods using a single projection cause undesired pixel displacement in various portions of an image, particularly in spherical images, as they fail to accurately represent different curvatures of near and distant objects, leading to inadequate presentation of visual content.
Innovation Solution
A system that simulates multiple lens distortions within images by determining multiple single lens distortion regions and boundary regions based on multi-lens distortion information, shifting pixels from input to output positions using a combination of single lens distortions and blends of lens distortions, allowing for continuous and smooth transitions between different distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single projection is used within an image, then the device complexity is reduced, but undesired pixel displacement occurs in one or more portions of the image
Solution Approach 1:
The image is divided into multiple regions, each processed with a different lens distortion model. The system segments the image based on detected lens distortion regions and applies appropriate distortion corrections to each segment, thereby reducing pixel displacement in different portions of the image while managing complexity through localized processing.
Solution Approach 2:
Different lens distortion models are applied to different regions of the image based on local characteristics. The system identifies regions with different distortion patterns and applies tailored distortion corrections to each region, ensuring high precision pixel displacement correction locally while maintaining overall system manageability.
2Manufacturing precision
If multiple lens distortions are simulated within an image, then the accuracy of representing different curvatures is improved, but the device complexity increases
Solution Approach 1:
The system segments the image into multiple lens distortion regions and processes each region with appropriate distortion models. This segmentation approach enables accurate representation of different curvatures in different parts of the image while managing complexity by treating each segment independently with suitable distortion parameters.
Solution Approach 2:
The system changes distortion parameters dynamically across different regions of the image. By adjusting lens distortion parameters based on detected region characteristics and blending between different distortion models in boundary regions, the system achieves accurate curvature representation while controlling complexity through parameter adaptation rather than fixed complex structures.
3Stability of the object's composition
If boundary regions use a blend of lens distortions, then seamless transitions between distortions are achieved, but computational complexity increases
Solution Approach 1:
Boundary regions act as intermediary zones between different lens distortion regions. The system blends distortion models in these boundary regions to create smooth transitions, using the boundary regions as mediators that gradually interpolate between different distortion parameters, thereby achieving seamless transitions while localizing the computational complexity to only the boundary areas.
Data Source
AI summary
Multiple single lens distortion regions and one or more boundary regions may be determined within an image. Individual single lens distortion regions may have pixel displacement defined by a single lens distortion and individual boundary regions may have pixel displacement defined by a blend of at least two lens distortions. Multiple lens distortions may be simulated within the image by shifting pixels of the image input positions to output positions based on locations of pixels within a single lens distortion region and the corresponding single lens distortion or within a boundary region and a blend of corresponding lens distortions.