Blind atmospheric turbulence deconvolution

A new blind image deconvolution technique is developed for atmospheric turbulence deblurring to overcome limitations of ‘generic’ blind deconvolution algorithms that do not take into account the complicated physics of the turbulence. The originality of the proposed approach relies on an actual physi...

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Bibliographic Details
Published in:IET image processing Vol. 14; no. 14; pp. 3422 - 3432
Main Authors: Deledalle, Charles-Alban, Gilles, Jérôme
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.12.2020
Subjects:
actual physical model
atmospheric turbulence
atmospheric turbulence deblurring
BATUD
blind atmospheric turbulence deconvolution algorithm
blind image deconvolution technique
complicated physics
deconvolution
Fried kernel
Gaussian mixture model
Gaussian processes
generic blind deconvolution algorithms
image quality metrics
image restoration
iterative algorithm
iterative methods
natural image patches
Research Article
simulated turbulence scenarios
iterative methods
image quality metrics
Fried kernel
BATUD
blind atmospheric turbulence deconvolution algorithm
complicated physics
simulated turbulence scenarios
atmospheric turbulence
iterative algorithm
blind image deconvolution technique
atmospheric turbulence deblurring
Gaussian mixture model
deconvolution
image restoration
natural image patches
Gaussian processes
generic blind deconvolution algorithms
actual physical model
ISSN:1751-9659, 1751-9667
Online Access:Get full text
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Summary:A new blind image deconvolution technique is developed for atmospheric turbulence deblurring to overcome limitations of ‘generic’ blind deconvolution algorithms that do not take into account the complicated physics of the turbulence. The originality of the proposed approach relies on an actual physical model, known as the Fried kernel, that quantifies the impact of the atmospheric turbulence on the optical resolution of images. While the original expression of the Fried kernel can seem cumbersome at first sight, the authors show that it can be reparameterised in a much simpler form. This simple expression allows to efficiently embed this kernel in the proposed blind atmospheric turbulence deconvolution (BATUD) algorithm. BATUD is an iterative algorithm that alternately performs deconvolution and estimates the Fried kernel by jointly relying on a Gaussian mixture model prior to natural image patches and controlling for the square Euclidean norm of the Fried kernel. Numerical experiments show that the proposed blind deconvolution algorithm behaves well in different simulated turbulence scenarios, as well as on real images. Not only BATUD outperforms state-of-the-art approaches used in atmospheric turbulence deconvolution in terms of image quality metrics but is also faster.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2019.1442