CCTV Radial Distortion Estimation Without Reference Image
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Solution Overview
Problem
Existing methods for image distortion correction in CCTV images require a reference image and involve complex computations, making them inefficient and resource-intensive.
Innovation Solution
A method and system for image distortion estimation that adds a certain distortion to the input image, detects outlines and straight lines, calculates their sums, and determines parameters of an objective function to estimate distortion without a separate reference image, using low-complexity computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a reference image is used to correct image distortion, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs self-calibration by using the captured image itself to estimate distortion parameters through iterative optimization, eliminating the need for external reference images or calibration tools. The distortion estimation is achieved by maximizing an objective function that measures straight line detection accuracy within the image content.
Solution Approach 2:
The patent creates a virtual reference by generating distorted versions of the input image with known distortion parameters, then uses these synthesized references to train the distortion estimation model without requiring physical reference images.
2Measurement precision
If complex computation is used to achieve accurate distortion correction, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system pre-calculates and stores distortion correction parameters for various distortion levels in lookup tables before actual image processing. During runtime, the system quickly searches these pre-computed tables to find the best matching distortion parameters, avoiding complex real-time calculations while maintaining accuracy.
Solution Approach 2:
The patent transforms the complex distortion correction problem into a parameter optimization task by representing distortion as a set of parameters (k1, k2, etc.) and using iterative optimization to find the parameters that maximize straight line detection, thereby simplifying the computational approach.
3Measurement precision
If iterative optimization is performed to determine distortion parameters, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs a limited number of iterative optimization steps (e.g., 10-100 iterations) rather than exhaustive optimization, achieving sufficient distortion parameter accuracy within a practical time frame. The optimization stops when convergence criteria are met or a maximum iteration limit is reached.
Data Source
AI summary
There are provided a method and a system for CCTV radial distortion estimation with low-complexity. An image distortion estimation method according to an embodiment includes: adding a certain distortion to an inputted image; detecting outlines from the image to which the distortion is added; detecting straight lines from the detected outlines; calculating a sum of the detected straight lines; performing the above operations N times, and determining parameters of an objective function by fitting an ‘objective function resulting from modeling of a sum of detected straight lines caused by an added distortion’ to the N distortions and the N sums; and estimating a distortion on the image by using the objective function the parameters of which are determined. Accordingly, the method does not need a cumbersome process since a separate reference image is not used, and is performed fast due to low-complexity of computation and less resources are required, and furthermore, relatively accurate distortion estimation is possible only with one image.