Non-iterative blind deconvolution algorithm based on power-law distribution

The spectral amplitude of most natural images is approximately isotropic and follows the power law. In this study, the authors propose a new non-iterative blind image deconvolution algorithm that builds an isosceles curve model to approximate the spectrum amplitude of the real image. In the authors’...

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Bibliographic Details
Published in:IET image processing Vol. 14; no. 17; pp. 4499 - 4506
Main Authors: Gao, Weizhe, Xu, Xuebin, Yang, Yikang, Zhang, Zhiguang
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 24.12.2020
Subjects:
approximation theory
blind deconvolution algorithm
curve model
deconvolution
estimated OTF
image deconvolution tasks
image restoration
integrated multidirectional comprehensive estimation
natural images
noniterative blind image deconvolution algorithm
optical transfer function
OTF estimation error
power‐law distribution
real‐time image restoration
Research Article
spectral amplitude
spectrum amplitude
Wiener filters
approximation theory
Wiener filters
power-law distribution
blind deconvolution algorithm
spectral amplitude
estimated OTF
image deconvolution tasks
deconvolution
real-time image restoration
curve model
optical transfer function
image restoration
OTF estimation error
natural images
noniterative blind image deconvolution algorithm
integrated multidirectional comprehensive estimation
spectrum amplitude
ISSN:1751-9659, 1751-9667
Online Access:Get full text
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Summary:The spectral amplitude of most natural images is approximately isotropic and follows the power law. In this study, the authors propose a new non-iterative blind image deconvolution algorithm that builds an isosceles curve model to approximate the spectrum amplitude of the real image. In the authors’ proposed algorithm, the optical transfer function (OTF) is obtained by comparing the reconstructed and degraded spectra. Then they employ the integrated multidirectional comprehensive estimation to reduce the OTF estimation error. The restored image is then obtained by applying the estimated OTF and the Wiener filter. Experiments on image deconvolution tasks indicate that the proposed algorithm provides a significant performance gain by obtaining an accurate OTF, reducing ringing artefacts compared with existing algorithms, and realising real-time image restoration.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2020.0647