Image Acquisition Motion Blur Correction via Cepstrum Analysis
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Solution Overview
Problem
Existing image acquisition methods struggle with effectively addressing motion blur caused by subject or device movement during image capture, particularly in blind deconvolution scenarios where motion parameters are unknown, leading to convergence issues, numerical instability, and high computation time.
Innovation Solution
A digital image acquisition apparatus that includes a motion detector to halt image capture when movement exceeds a threshold, a motion extractor to determine point spread function (PSF) using Cepstrum analysis, and an image re-constructor to correct images, merging multiple images to produce a high-quality image with reduced motion blur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If blind deconvolution is used to restore images degraded by motion blur, then image quality can be improved, but convergence problems and numerical instability occur
Solution Approach 1:
The patent segments the blind deconvolution process into two distinct phases: first identifying motion parameters (PSF) separately from the degraded image using spectral analysis, then applying these parameters in a subsequent image restoration process. This segmentation resolves convergence problems by decoupling the parameter identification from the restoration process, allowing each to be optimized independently.
Solution Approach 2:
The patent performs preliminary identification of motion parameters (PSF) before conducting the image restoration process. By using spectral analysis and Cepstrum techniques to determine the PSF in advance, the restoration process can proceed with known parameters, avoiding the numerical instability associated with simultaneous estimation.
2Measurement precision
If iterative methods are used for blind deconvolution, then motion parameters and true image can be simultaneously estimated, but extremely high computation time is required
Solution Approach 1:
The patent extracts the identification of motion parameters from the iterative restoration process and performs it separately using spectral analysis. The Cepstrum-based method quickly identifies the PSF without requiring iterative optimization, significantly reducing computation time while maintaining accuracy.
Solution Approach 2:
The patent replaces the computationally intensive iterative mechanical optimization process with a spectral analysis approach using Cepstrum techniques. This substitution transforms the problem from one requiring repeated numerical optimization to one solvable through frequency domain analysis, dramatically reducing computation time.
3Productivity
If spectral analysis is used to estimate PSF from Cepstrum, then fast and straightforward results are obtained, but good results are achieved only for uniform and linear motion
Solution Approach 1:
The patent enhances the spectral analysis method by dynamically selecting which Cepstrum spikes to use based on their relevance to the actual motion. The system evaluates spike patterns and selects those corresponding to the true motion characteristics, allowing adaptation to non-uniform and non-linear motion while maintaining processing speed.
Solution Approach 2:
The patent modifies the spectral analysis approach by changing how PSF parameters are derived from the Cepstrum. Instead of using fixed assumptions about uniform motion, the method dynamically adjusts parameter selection based on observed spike patterns, enabling accurate PSF estimation for various motion types including non-linear trajectories.
4Manufacturing precision
If multiple short-exposure images are captured and merged, then higher quality image can be produced, but the number of images required increases computational complexity
Solution Approach 1:
The patent performs preliminary motion parameter identification on individual short-exposure images before merging. By determining the PSF for each image in advance using spectral analysis, the merging process becomes simpler as it only needs to combine images with known motion characteristics rather than performing complex joint optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach minimizes the number of short-exposure images needed, allows for flexible selection of images for final image creation, and effectively handles both linear and non-linear motion, improving image quality and reducing computational complexity.
Implementation Method 1
A motion extractor determines motion parameters of a selected image stored in the image store
Data Source
AI summary
An image acquisition sensor of a digital image acquisition apparatus is coupled to imaging optics for acquiring a sequence of images. Images acquired by the sensor are stored. A motion detector causes the sensor to cease capture of an image when the degree of movement in acquiring the image exceeds a threshold. A controller selectively transfers acquired images for storage. A motion extractor determines motion parameters of a selected, stored image. An image re-constructor corrects the selected image with associated motion parameters. A selected plurality of images nominally of the same scene are merged and corrected by the image re-constructor to produce a high quality image of the scene.