Detection and Classification of Invariant Blurs
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概要
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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.
- 2009-12-01
著者
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TANAKA TOSHIHISA
Department of Clinical Neuroscience & Psychiatry, Osaka University Graduate School of Medicine
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CHONG Rachel
Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology
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Chong Rachel
Tokyo Univ. Agriculture And Technol. Koganei‐shi Jpn
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Chong Rachel
Department Of Electrical And Electronic Engineering Tokyo University Of Agriculture And Technology
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