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Image deconvolution by means of frequency blur invariant concept
Barmak Honarvar Shakibaei1, Peyman Jahanshahi1
1Integrated Lightwave Research Group, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia.
Thescientificworldjournal
|September 10, 2014
Summary
This study introduces a novel frequency framework for developing blur invariant features. These features enable accurate estimation of the point spread function (PSF) and deconvolution of Gaussian blurred images.
Area of Science:
- Image Processing
- Computer Vision
- Signal Processing
Background:
- Existing blur invariant descriptors are limited to spatial or moment domains.
- Gaussian blur significantly degrades image quality, necessitating effective deconvolution techniques.
Purpose of the Study:
- To propose a frequency framework for developing novel blur invariant features.
- To enable accurate estimation of the point spread function (PSF) and perform image deconvolution.
Main Methods:
- Developed blur invariant features using a frequency framework.
- Established an equivalent relationship between normalized Fourier transforms of blurred and original images.
- Normalized Fourier transforms by fixed frequencies set to one.
Main Results:
- The proposed frequency invariant descriptors allow for the estimation of both PSF and the original image.
- Experimental results demonstrate the effectiveness of the frequency invariants for image deconvolution.
Conclusions:
- The proposed frequency framework offers a robust method for creating blur invariant features.
- These features are effective for image deconvolution and PSF estimation, outperforming existing methods.
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