Abstract
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.
Highlights
Deblurring a grey-scale image consists in recovering a sharp image on the basis of a single blurred observation
In the case of uniform defocus blur, the physical process that leads to a defocused image is typically modeled by convolution of the original image with a point-spread function (PSF) plus additive noise [2]
The left-hand side of Figure 1 shows a schematic of such model, where the original image intensity is denoted by f and the blurred image intensity is denoted by g
Summary
Deblurring a grey-scale image consists in recovering a sharp image on the basis of a single blurred observation (possibly corrupted by disturbances). Blurring artifacts are caused by defocus aberration or motion blur [1]. In the case of uniform defocus blur, the physical process that leads to a defocused image is typically modeled by convolution of the original image with a point-spread function (PSF) plus additive noise [2]. The left-hand side of Figure 1 shows a schematic of such model, where the original image intensity is denoted by f and the blurred image intensity is denoted by g. A closely related problem is blind deblurring from more than a single out-of-focus observation of a sharp image [3]. Motion blur may be modeled as the integration over a light field captured at each time during exposure [4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
