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Improvement and Assessment of a Blind Image Deblurring Algorithm Based on Independent Component Analysis

Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche s.n.c., 60131 Ancona, Italy
Academic Editor: Yudong Zhang
Received: 22 April 2021 / Revised: 1 June 2021 / Accepted: 25 June 2021 / Published: 1 July 2021
(This article belongs to the Section Computational Engineering)
The aim of the present paper is to improve an existing blind image deblurring algorithm, based on an independent component learning paradigm, by manifold calculus. The original technique is based on an independent component analysis algorithm applied to a set of pseudo-images obtained by Gabor-filtering a blurred image and is based on an adapt-and-project paradigm. A comparison between the original technique and the improved method shows that independent component learning on the unit hypersphere by a Riemannian-gradient algorithm outperforms the adapt-and-project strategy. A comprehensive set of numerical tests evidenced the strengths and weaknesses of the discussed deblurring technique. View Full-Text
Keywords: blind image deblurring; Gabor filter; independent component analysis; Riemannian manifold; gradient-based learning blind image deblurring; Gabor filter; independent component analysis; Riemannian manifold; gradient-based learning
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MDPI and ACS Style

Fiori, S. Improvement and Assessment of a Blind Image Deblurring Algorithm Based on Independent Component Analysis. Computation 2021, 9, 76. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070076

AMA Style

Fiori S. Improvement and Assessment of a Blind Image Deblurring Algorithm Based on Independent Component Analysis. Computation. 2021; 9(7):76. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070076

Chicago/Turabian Style

Fiori, Simone. 2021. "Improvement and Assessment of a Blind Image Deblurring Algorithm Based on Independent Component Analysis" Computation 9, no. 7: 76. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070076

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