Using a Blur Metric to Estimate Linear Motion Blur Parameters

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called...

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Autores principales:	Taiebeh Askari Javaran, Hamid Hassanpour
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/d12c5d15a2244e56a9ac6e3a02e1bf3a
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spelling	oai:doaj.org-article:d12c5d15a2244e56a9ac6e3a02e1bf3a2021-11-08T02:36:23ZUsing a Blur Metric to Estimate Linear Motion Blur Parameters1748-671810.1155/2021/6048137https://doaj.org/article/d12c5d15a2244e56a9ac6e3a02e1bf3a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6048137https://doaj.org/toc/1748-6718Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.Taiebeh Askari JavaranHamid HassanpourHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Taiebeh Askari Javaran Hamid Hassanpour Using a Blur Metric to Estimate Linear Motion Blur Parameters
description	Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.
format	article
author	Taiebeh Askari Javaran Hamid Hassanpour
author_facet	Taiebeh Askari Javaran Hamid Hassanpour
author_sort	Taiebeh Askari Javaran
title	Using a Blur Metric to Estimate Linear Motion Blur Parameters
title_short	Using a Blur Metric to Estimate Linear Motion Blur Parameters
title_full	Using a Blur Metric to Estimate Linear Motion Blur Parameters
title_fullStr	Using a Blur Metric to Estimate Linear Motion Blur Parameters
title_full_unstemmed	Using a Blur Metric to Estimate Linear Motion Blur Parameters
title_sort	using a blur metric to estimate linear motion blur parameters
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d12c5d15a2244e56a9ac6e3a02e1bf3a
work_keys_str_mv	AT taiebehaskarijavaran usingablurmetrictoestimatelinearmotionblurparameters AT hamidhassanpour usingablurmetrictoestimatelinearmotionblurparameters
_version_	1718443140694671360

Using a Blur Metric to Estimate Linear Motion Blur Parameters

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