Motion Blur Detection via Radon Transform Analysis
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Solution Overview
Problem
Existing image processing technologies face challenges in effectively addressing motion blur in biometric images, particularly in iris recognition systems, where motion blur degrades the quality of images captured from moving objects, affecting the accuracy of identification and authentication processes.
Innovation Solution
A software-based method for estimating the extent and direction of motion blur using a data processing system, which applies the Radon transform and Fourier spectrum analysis to determine the blur, allowing for the correction or compensation of blurred images, and can be integrated into image processing systems for real-time applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to address motion blur, then processing can be performed, but the accuracy and reliability of motion blur detection is insufficient
Solution Approach 1:
The patent segments the image processing task into distinct frequency domain analyses. It separates the power spectrum analysis from the phase spectrum analysis, and further divides the processing into radial frequency components. This segmentation allows each component to be optimized independently, improving overall detection accuracy and reliability of motion blur parameters.
Solution Approach 2:
The patent transforms the motion blur detection problem from the spatial domain to the frequency domain using Fourier transform. By analyzing the power spectrum and phase spectrum in the frequency domain, the method achieves more accurate motion blur parameter estimation compared to traditional spatial domain methods, resolving the contradiction between measurement precision and reliability.
2Manufacturing precision
If existing motion blur correction techniques are applied, then some blur compensation is achieved, but the process is computationally intensive and slow
Solution Approach 1:
The patent performs preliminary analysis of the power spectrum and phase spectrum to directly estimate motion blur parameters before actual correction is applied. By pre-determining the motion blur characteristics through frequency domain analysis, the system can apply targeted correction with fewer computational iterations, improving processing speed while maintaining image resolution accuracy.
Solution Approach 2:
The patent changes the approach from iterative spatial domain correction to direct frequency domain parameter estimation. By analyzing radial frequency components and phase relationships, the system directly calculates motion blur parameters (direction and extent) without requiring multiple iterative corrections, significantly improving processing productivity while maintaining manufacturing precision.
3Reliability
If comprehensive image quality processing is performed, then various image defects can be addressed, but the complexity of the processing system increases
Solution Approach 1:
The patent extracts and focuses specifically on the frequency domain characteristics (power spectrum and phase spectrum) that are most indicative of motion blur. By isolating and analyzing only the relevant spectral components rather than processing the entire image through multiple complex stages, the system achieves reliable image quality assessment with reduced processing complexity.
Solution Approach 2:
The patent moves the image quality assessment to the frequency domain, where motion blur characteristics manifest as distinct patterns in the power spectrum and phase spectrum. This dimensional transformation simplifies the detection process by converting a complex spatial domain problem into a more tractable frequency domain analysis, reducing system complexity while maintaining reliability.
Data Source
AI summary
A system for detecting motion blur may include a process in which one or more digital images taken by a camera having a shutter are obtained, wherein the digital image depicts objects in a physical world. Further, the system may estimate the motion blur in the digital image using a ratio of one or more values obtained from the projection of a 2D spectrum of the image and a Fourier transform of a sequence of the shutter used in obtaining the image.