Camera Geometric Aberration Correction via Image-Point Density Modeling
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Solution Overview
Problem
Existing image sharpening methods are inadequate for capturing high-quality images with minimal effort when there is relative movement between the object and the camera, particularly in applications like photogrammetry where image sharpness and object structure positioning are critical.
Innovation Solution
The use of an image-point-dependent density function in a mathematical model to account for the varying influence of the camera on exposure across different image points, allowing for more effective correction of image blurring beyond previous limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image sharpening methods are used to correct blurring after capture, then image sharpness can be improved, but the methods are inadequate for complex camera movements and require significant computational effort
Solution Approach 1:
The patent applies preliminary action by using an image-point-dependent density function to pre-model the exposure variations and camera movement effects during the image capture process. This mathematical model is prepared in advance to account for varying influence across different image points, enabling more efficient deconvolution operations later without requiring complex real-time computations during the sharpening process.
2Measurement precision
If forward motion compensation is used to reduce blurring during capture, then image sharpness is improved, but the system complexity and cost increase
Solution Approach 1:
The patent replaces mechanical forward motion compensation systems with a mathematical modeling approach. Instead of using complex mechanical drive systems to coordinate the image sensor movement with vehicle movement, the invention uses computational methods with image-point-dependent density functions to model and correct the blurring effects after capture, thereby reducing device complexity while maintaining image sharpness.
3Measurement precision
If a stabilizing camera suspension is used to compensate for disruptive movements, then image blurring is reduced, but the technical limitations prevent complete correction and system complexity increases
Solution Approach 1:
The patent replaces mechanical stabilizing camera suspension systems with a mathematical modeling approach. Instead of relying on complex mechanical suspension systems that have technical limitations, the invention uses image-point-dependent density functions to model the exposure variations caused by camera movements and applies deconvolution algorithms to correct the blurring, thereby reducing device complexity while achieving complete correction.
4Productivity
If conventional image sharpening with constant density function is used, then computational effort is reduced, but image sharpness and correction accuracy are insufficient
Solution Approach 1:
The patent applies local quality by using an image-point-dependent density function that varies across different locations in the image. Instead of using a constant density function that treats all image points uniformly, the invention models the varying influence of the camera on exposure at different image points, allowing for more accurate correction of image blurring while maintaining computational efficiency through the structured mathematical approach.
Data Source
AI summary
A method for correcting a geometric imaging aberration of a camera includes the steps of modeling deformation of a transparent cover and/or sensor layer caused by a pressure difference between an internal pressure in the sensor recess and an ambient pressure of the camera with a deformation model; determining a pressure difference based on the internal pressure and ambient pressure of the camera; determining a deformation of the transparent cover and/or the sensor layer with the pressure difference using the deformation model; determining a shift of the mapping of the object point onto an actual picture point in the image recording caused by the deformation as a geometric imaging aberration; and correcting the geometric imaging aberration by shifting the mapping of the object point onto the actual picture point resulting in the image recording in the corrected image recording to another picture point by the geometric imaging aberration.