Image Deblurring With Image-Point Exposure Density Modeling
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Solution Overview
Problem
Existing image sharpening methods fail to adequately correct image blurring caused by relative movement between a camera and an object during exposure, particularly in applications requiring high image sharpness like photogrammetry, and often require complex and costly solutions.
Innovation Solution
A method using an image-point-dependent density function to model the influence of the camera's exposure on different image points, allowing for improved image correction by dividing the image area into sections with varying density functions, and employing mathematical deconvolution to reconstruct a sharpened image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image sharpening methods are used to correct image blurring, then image sharpness is improved, but the blur kernel is usually not known requiring complex blind deconvolution
Solution Approach 1:
The patent applies preliminary action by using inertial sensors to measure camera movement during exposure and pre-calculating the blur kernel before deconvolution. This eliminates the need for complex blind deconvolution by having the motion information available in advance, directly resolving the technical contradiction between achieving image sharpness and avoiding computational complexity.
2Manufacturing precision
If forward motion compensation is used to reduce image blurring, then image sharpness is improved, but the system complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical forward motion compensation system with a computational approach using inertial sensors and deconvolution algorithms. Instead of physically moving the image sensor to track object points, the system uses sensor data to model and correct blurring mathematically, significantly reducing mechanical complexity while maintaining image sharpness.
Solution Approach 2:
The patent introduces inertial sensors as an intermediary between the camera movement and the image processing. These sensors measure the camera's motion during exposure and provide this information to the deconvolution algorithm, acting as a mediator that enables accurate blur correction without requiring direct mechanical coupling between the camera and image sensor.
3Manufacturing precision
If a stabilizing camera suspension is used to compensate for vehicle movements, then some image blurring is reduced, but technical limitations prevent adequate correction and system complexity increases
Solution Approach 1:
The patent replaces the mechanical camera suspension system with a computational correction approach. Instead of using complex mechanical devices to physically stabilize the camera, the system uses inertial sensors to measure movements and applies deconvolution algorithms to correct the resulting blur digitally, eliminating the need for complex mechanical stabilization while achieving superior image sharpness.
Data Source
AI summary
A method for correcting image blurring to make high-quality captured images possible in the event of a relative movement between an object to be captured and a camera, a method for correcting image blurring includes modeling a correlation between an image point in the blurred captured image and an image point of a sharpened captured image with a mathematical model, wherein the model takes into account the relative movement between the camera and the object during the exposure time and contains a density function which describes an influence of the camera on the exposure during the exposure time, and wherein in the mathematical model an image-point-dependent density function is used by means of which a different influence of the camera on the exposure of different image points of the captured image is taken into account during the image correction.