Open AccessNon‐iterative blind deconvolution algorithm based on power‐law distribution
Author(s) -
Gao Weizhe,
Xu Xuebin,
Yang Yikang,
Zhang Zhiguang
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0647
Subject(s) - deconvolution , blind deconvolution , wiener deconvolution , algorithm , wiener filter , optical transfer function , image restoration , mathematics , ringing , iterative method , spectral density , filter (signal processing) , image (mathematics) , artificial intelligence , computer science , image processing , computer vision , statistics , mathematical analysis
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.