Off-Center ROI Geometric Correction for Nonlinear Lens Distortion
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Solution Overview
Problem
Wide field of view lenses, such as fish-eye lenses, suffer from varying image quality across different field angles due to non-linear geometries, leading to distortion and challenges in face tracking and image correction, especially in off-center peripheral regions.
Innovation Solution
A method is introduced to enhance image quality by determining and reconstructing off-center regions of interest (ROIs) using geometric correction, applying face detection and tracking, and compensating for global motion, while employing a combination of optical and post-processing techniques to map the image scene onto the sensor, including rectilinear and cylindrical projections to minimize distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a wide field of view non-linear lens is used to capture a large scene, then the field of view coverage is improved, but the image quality and resolution in off-center peripheral regions deteriorate due to distortion
Solution Approach 1:
The patent divides the image processing into multiple stages: initial geometric correction to remove gross distortion, followed by selective super-resolution processing for specific regions of interest. This segmentation allows different processing strategies to be applied to different parts of the image, maintaining overall field of view while improving peripheral quality where needed.
Solution Approach 2:
The patent applies super-resolution processing selectively to regions of interest rather than uniformly across the entire image. By identifying off-center peripheral regions that require enhancement and applying computational techniques specifically to those areas, the system improves local image quality without unnecessarily processing the entire wide field of view.
2Manufacturing precision
If geometric correction is applied to correct distortion in non-linear lens images, then the distortion is reduced, but computational complexity and processing time increase
Solution Approach 1:
The patent performs initial geometric correction as a preliminary step before applying more computationally intensive super-resolution techniques. By removing gross distortion first, the subsequent processing operates on a pre-corrected image, reducing the computational burden of later stages.
Solution Approach 2:
The patent applies super-resolution processing only to specific regions of interest rather than the entire image. This partial action approach reduces computational complexity by focusing resources only on areas where enhancement is needed, rather than uniformly processing the complete wide field of view image.
3Measurement precision
If super-resolution processing is applied to off-center regions to improve pixel quality, then the resolution in peripheral regions is enhanced, but the computational burden increases
Solution Approach 1:
The patent applies super-resolution processing selectively to off-center peripheral regions of interest rather than uniformly across the entire image. This localized approach enhances pixel reconstruction quality in specific areas while minimizing the overall computational burden by avoiding unnecessary processing of regions that already have adequate quality.
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 frame of reference as a reconstructed ROI. A quality of reconstructed pixels is determined within the reconstructed ROI. Image analysis is selectively applied to the reconstructed ROI based on the quality of the reconstructed pixels.