Image Distortion Correction with Region-Specific Mesh Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Image distortion occurs due to optical characteristics of lenses during image generation, including lens distortion and perspective distortion, which vary based on lens type and object distance, affecting image quality.
Innovation Solution
An electronic apparatus employs a method to correct image distortion by generating a projection image, creating a cognitive map that divides the image into areas of different types, and using mesh information with varying pixel movement levels to generate an output image, minimizing distortion while preserving recognizable features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform distortion correction is applied to the entire image, then perspective distortion is reduced, but recognizable features and object characteristics may be lost or distorted
Solution Approach 1:
The image is divided into multiple regions based on object types and distances. Different mesh information is generated for each region, allowing selective application of distortion correction. This segmentation enables the system to preserve recognizable features in certain regions while correcting distortion in others, resolving the contradiction between correction accuracy and feature preservation.
Solution Approach 2:
Different levels of distortion correction are applied to different parts of the image based on local characteristics. The system generates multiple types of mesh information corresponding to different object types and applies appropriate correction strength to each region. This local quality approach ensures that recognizable features are preserved where needed while distortion is corrected where appropriate.
2Measurement precision
If distortion correction is applied to all areas equally, then overall image distortion is reduced, but processing complexity increases
Solution Approach 1:
The image processing is segmented into different regions with different correction requirements. By dividing the image based on object types and distances, the system can apply simplified correction models to each segment rather than complex uniform correction to the entire image, reducing overall processing complexity while maintaining correction quality.
Solution Approach 2:
The system applies distortion correction selectively to only those regions where it is most needed, rather than uniformly to the entire image. By identifying key regions with significant distortion and applying correction primarily there, the system reduces processing complexity while achieving sufficient overall correction效果.
3Measurement precision
If detailed mesh information is generated for all pixel positions, then correction precision is improved, but computational load increases
Solution Approach 1:
The computational domain is segmented into different regions based on object types and distances. Mesh information is generated at different levels of detail for different segments, with higher precision allocated to regions containing important objects and lower precision to background regions. This segmentation reduces total computational load while maintaining precision where it matters most.
Solution Approach 2:
Different levels of mesh detail are applied to different regions of the image. The system generates detailed mesh information locally for regions containing recognizable features and objects, while using coarser mesh information for other regions. This local quality approach optimizes the balance between correction precision and computational load.
Data Source
AI summary
A method and electronic apparatus may be provided, the method includes obtaining a first image, generating a projection image based on the first image, in which perspective distortion of the first image is corrected, generating a cognitive map according to a plurality of objects in the first image, wherein the cognitive map divides the first image into a plurality of areas of a first plurality of types, generating first mesh information for correcting the first image based on the projection image and the cognitive map, wherein the first mesh information comprises movement information for a plurality of pixel positions, and generating an output image by correcting the first image based on the first mesh information.