Superresolution Enhancement of Off-Center ROIs in Wide Field View Lenses
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Solution Overview
Problem
Wide field of view lenses suffer from varying image quality across different field angles due to non-linear imaging systems, leading to distortion and challenges in correcting global motion errors, particularly affecting peripheral regions.
Innovation Solution
A method and device for enhancing image quality by determining and reconstructing off-center regions of interest (ROIs) using geometric correction, compensating for global motion, and applying image analysis to generate an enhanced output image, incorporating a non-linear wide-angled lens and imaging sensor system with processor-driven algorithms for selective image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If a non-linear wide field of view lens is used to capture a large field of view, then the field angle coverage is improved, but the image quality and resolution in peripheral regions deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct stages: capturing the wide field of view image, detecting global motion, performing geometrical correction to compensate for distortion, and then applying super-resolution enhancement. This segmented approach allows each processing stage to address specific quality issues in peripheral regions while maintaining the overall wide field of view capability.
Solution Approach 2:
The patent implements preliminary action by performing geometrical correction before super-resolution enhancement. The global motion detection and correction parameters are determined in advance, and the image is pre-processed to compensate for radial distortion and chromatic aberration before the final enhancement step. This preliminary correction prepares the peripheral regions for better quality enhancement in subsequent processing.
2Shape
If geometrical correction is applied to correct distortion in non-linear lens images, then the distortion is reduced, but global motion errors are introduced or exacerbated
Solution Approach 1:
The patent employs feedback by using the captured wide field of view image itself to detect global motion and determine correction parameters. The system analyzes the image data to identify motion patterns and distortion characteristics, then uses this feedback information to adjust the geometrical correction process. This closed-loop approach ensures that the correction adapts to actual image conditions rather than applying fixed transformations.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting correction parameters based on detected global motion. The correction model uses variable parameters that are determined from the image data, allowing the system to adapt the geometrical correction to different motion conditions and field angles. This parameter adaptation enables the system to maintain accuracy across varying operational conditions.
3Manufacturing precision
If super-resolution enhancement is applied to peripheral regions, then the resolution is improved, but the processing complexity and time increase
Solution Approach 1:
The patent applies local quality by focusing super-resolution enhancement specifically on peripheral regions of the image where resolution degradation is most pronounced. Rather than uniformly processing the entire image, the system identifies and targets the off-center regions that require enhancement, applying computationally intensive algorithms only where needed. This localized approach improves peripheral resolution while minimizing overall processing complexity.
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. A quality of reconstructed pixels is determined within the reconstructed ROI. One or more additional initially distorted images is/are acquired, and matching additional ROIs are extracted and reconstructed to combine with reduced quality pixels of the first reconstructed ROI using a super-resolution technique to provide one or more enhanced ROIs.