Antecedents of Trust and the Continuance Intention in IoT-Based Smart Products: The Case of Consumer Wearables
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Publication Details
Author list: Pal D., Funilkul S., Papasratorn B.
Publisher: Taylor and Francis Ltd.
Publication year: 2019
Volume number: 7
Start page: 184160
End page: 184171
Number of pages: 12
ISSN: 1741-5977
eISSN: 1741-5977
Languages: English-Great Britain (EN-GB)
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Abstract
Restoring images corrupted by Poisson noise have attracted much attention in recent years due to its significant applications in image processing. There are various regularization methods of solving this problem and one of the most famous is the total variation (TV) model. In this paper, we present a new method based on accelerated alternating minimization algorithm (AAMA) which involves minimizing the sum of a Kullback–Leibler divergence term and a TV term for restoring Poisson noise degraded images. Our proposed algorithm is applied in solving the aforementioned problem and its convergence analysis is established under very weak conditions. In addition, the numerical examples reported demonstrate the efficiency and versatility of our method compared to existing methods of restoring images with Poisson noise. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Keywords
alternating direction method, correction procedure, Poisson noise images
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