Off-Center ROI Reconstruction for Nonlinear Lens Tracking
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Solution Overview
Problem
Wide field of view imaging systems with non-linear lenses face challenges in maintaining consistent image quality across the field of view, particularly in off-center peripheral regions, due to distortion and global motion, which affects object detection and tracking accuracy.
Innovation Solution
A method is provided to enhance image quality in off-center peripheral regions by determining and reconstructing regions of interest (ROIs) using geometric correction, compensating for global motion, and applying super-resolution techniques to improve pixel quality, allowing for effective object tracking and detection even in reduced quality areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a wide field of view non-linear lens is used to capture the entire scene, then the field of view coverage is improved, but the image quality and resolution in off-center peripheral regions deteriorate
Solution Approach 1:
The image frame is divided into multiple regions of interest (ROIs) based on detected objects. Only these segmented ROIs are extracted and processed through geometric correction and super-resolution, rather than processing the entire wide-field image. This allows high-quality processing to be applied selectively to important regions while maintaining overall wide-field coverage.
Solution Approach 2:
Different processing quality levels are applied to different regions of the image. ROIs containing detected objects receive full geometric correction and super-resolution processing for high quality, while other peripheral regions maintain lower quality. This creates local quality variation optimized for the specific application needs.
2Measurement precision
If geometric correction is applied to correct lens distortion, then the distortion accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
Geometric correction is applied only to extracted ROIs rather than the entire image frame. This segmentation approach maintains high distortion correction accuracy for objects of interest while significantly reducing the computational complexity by limiting the processing area to small regions rather than the full wide-field image.
3Measurement precision
If super-resolution techniques are applied to enhance pixel quality in peripheral regions, then the image resolution is improved, but the processing time and computational resources increase
Solution Approach 1:
Super-resolution processing is applied only to extracted ROIs containing detected objects rather than the entire image. This segmentation strategy maintains high pixel quality enhancement for important regions while reducing processing time by limiting the super-resolution computation to small localized areas.
Solution Approach 2:
Instead of applying super-resolution uniformly across the entire image, the technique applies processing partially only where needed (in ROIs). This partial action approach achieves sufficient pixel quality improvement for object regions without the excessive processing time that would result from full-image application.
4Measurement precision
If motion compensation is applied to correct global motion effects, then the tracking accuracy is improved, but the device complexity and processing requirements increase
Solution Approach 1:
Motion compensation is applied selectively to ROIs rather than the entire image sequence. This segmentation approach maintains high tracking accuracy for detected objects while reducing processing requirements by limiting motion analysis and compensation to small regional areas rather than global image processing.
Data Source
AI summary
A technique of enhancing a scene containing one or more off-center peripheral regions within an initial distorted image captured with a large field of view includes determining and extracting an off-center region of interest (hereinafter “ROI”) within the image. Geometric correction is applied to reconstruct the off-center ROI into a rectangular or otherwise undistorted or less distorted frame of reference as a reconstructed ROI.