Geometrical Distortion Engine Tile Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image acquisition systems fail to dynamically adjust for multiple sources of distortion, such as wide field of view lens systems, camera shake, and user-induced distortions, leading to suboptimal image correction and display.
Innovation Solution
A Geometrical Distortion Engine (GDE) that processes images tile-by-tile, using a Grid Formatter Unit (GFU) and Geometrical Distortion Core (GDC) to apply affine and global transformations, along with Low Level Distortion Descriptors (LLDD), to correct distortions and generate a rectilinear grid for display or storage, while accommodating user-defined distortions and lens-sensor misalignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed mapping between sensor surface and display grid is used, then device complexity is reduced, but adaptability to different distortion sources deteriorates
Solution Approach 1:
The image processing is divided into independent tile units that can be processed separately. Each tile contains distortion correction information for a specific region, allowing the system to handle multiple distortion sources through modular processing rather than requiring a complex unified correction mechanism.
Solution Approach 2:
Distortion correction parameters and mapping information are pre-calculated and stored in the tile data before runtime processing. This preliminary preparation allows the system to adapt to different distortion sources without requiring complex real-time calculations, reducing operational complexity while maintaining high adaptability.
2Manufacturing precision
If distortion correction is applied to the entire image, then correction completeness is improved, but processing time increases
Solution Approach 1:
The image is divided into multiple tiles that can be processed in parallel. This segmentation allows the system to maintain high correction quality across the entire image while reducing total processing time through concurrent execution of tile processing operations.
Solution Approach 2:
Each tile is processed with locally optimized correction parameters tailored to its specific region and distortion characteristics. This local quality approach ensures high correction accuracy for each tile while avoiding the overhead of applying uniform complex corrections to the entire image.
3Measurement precision
If high-resolution pixel mapping is used, then image quality is improved, but memory requirements increase
Solution Approach 1:
Pixel mapping data is segmented into tile-specific structures that only store information for local regions. This segmentation reduces overall memory requirements while maintaining high mapping precision within each tile, as each tile contains only the necessary high-resolution mapping data for its specific area.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An image acquisition system comprising a first memory for storing at least a portion of a distorted input image acquired from an image sensor and a lens system, a second memory for writing corrected output image information, and an interpolator module connected to both memories comprising a bi-cubic interpolator and a pair of bi-linear interpolators and being switchable between a first high quality mode and a second high speed mode.