Non‐iterative blind deconvolution algorithm based on power‐law distribution. Issue 17 (25th February 2021)

• Record Type: Journal Article
• Authors: Gao, Weizhe; Xu, Xuebin; Yang, Yikang; Zhang, Zhiguang
• Is Part Of: IET image processing. Volume 14:Issue 17(2020)
• Page Start: 4499
• Page End: 4506
• Publication Date: 2021-02-25
• Subjects: image restoration -- optical transfer function -- deconvolution -- Wiener filters -- approximation theory; blind deconvolution algorithm -- power‐law distribution -- spectral amplitude -- natural images -- noniterative blind image deconvolution algorithm -- curve model -- spectrum amplitude -- optical transfer function -- integrated multidirectional comprehensive estimation -- OTF estimation error -- estimated OTF -- image deconvolution tasks -- real‐time image restoration; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0647
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.