The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Um novo algoritmo para detectar e identificar simultaneamente borrões invariantes é proposto. Isto se baseia principalmente no comportamento dos valores extremos em uma imagem. É computacionalmente simples e rápido, tornando-o adequado para pré-processamento, especialmente em aplicações práticas de imagem. Os benefícios da utilização deste método incluem a eliminação de processos desnecessários, uma vez que as imagens não desfocadas serão separadas das imagens desfocadas que requerem desconvolução. Além disso, pode melhorar o desempenho da reconstrução através da identificação adequada do tipo de desfoque, para que um algoritmo de desconvolução específico de desfoque mais eficaz possa ser aplicado. Resultados experimentais em imagens naturais e suas versões desfocadas sinteticamente mostram as características e validade do método proposto. Além disso, pode-se observar que a seleção de características torna o método mais eficiente e eficaz.
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Rachel Mabanag CHONG, Toshihisa TANAKA, "Detection and Classification of Invariant Blurs" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 12, pp. 3313-3320, December 2009, doi: 10.1587/transfun.E92.A.3313.
Abstract: A new algorithm for simultaneously detecting and identifying invariant blurs is proposed. This is mainly based on the behavior of extrema values in an image. It is computationally simple and fast thereby making it suitable for preprocessing especially in practical imaging applications. Benefits of employing this method includes the elimination of unnecessary processes since unblurred images will be separated from the blurred ones which require deconvolution. Additionally, it can improve reconstruction performance by proper identification of blur type so that a more effective blur specific deconvolution algorithm can be applied. Experimental results on natural images and its synthetically blurred versions show the characteristics and validity of the proposed method. Furthermore, it can be observed that feature selection makes the method more efficient and effective.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.3313/_p
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@ARTICLE{e92-a_12_3313,
author={Rachel Mabanag CHONG, Toshihisa TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Detection and Classification of Invariant Blurs},
year={2009},
volume={E92-A},
number={12},
pages={3313-3320},
abstract={A new algorithm for simultaneously detecting and identifying invariant blurs is proposed. This is mainly based on the behavior of extrema values in an image. It is computationally simple and fast thereby making it suitable for preprocessing especially in practical imaging applications. Benefits of employing this method includes the elimination of unnecessary processes since unblurred images will be separated from the blurred ones which require deconvolution. Additionally, it can improve reconstruction performance by proper identification of blur type so that a more effective blur specific deconvolution algorithm can be applied. Experimental results on natural images and its synthetically blurred versions show the characteristics and validity of the proposed method. Furthermore, it can be observed that feature selection makes the method more efficient and effective.},
keywords={},
doi={10.1587/transfun.E92.A.3313},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Detection and Classification of Invariant Blurs
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3313
EP - 3320
AU - Rachel Mabanag CHONG
AU - Toshihisa TANAKA
PY - 2009
DO - 10.1587/transfun.E92.A.3313
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2009
AB - A new algorithm for simultaneously detecting and identifying invariant blurs is proposed. This is mainly based on the behavior of extrema values in an image. It is computationally simple and fast thereby making it suitable for preprocessing especially in practical imaging applications. Benefits of employing this method includes the elimination of unnecessary processes since unblurred images will be separated from the blurred ones which require deconvolution. Additionally, it can improve reconstruction performance by proper identification of blur type so that a more effective blur specific deconvolution algorithm can be applied. Experimental results on natural images and its synthetically blurred versions show the characteristics and validity of the proposed method. Furthermore, it can be observed that feature selection makes the method more efficient and effective.
ER -