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A Blur Classification Approach Using Deep Convolution Neural Network

A Blur Classification Approach Using Deep Convolution Neural Network

Shamik Tiwari
Copyright: © 2020 |Volume: 11 |Issue: 1 |Pages: 19
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781799806851|DOI: 10.4018/IJISMD.2020010106
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MLA

Tiwari, Shamik. "A Blur Classification Approach Using Deep Convolution Neural Network." IJISMD vol.11, no.1 2020: pp.93-111. http://doi.org/10.4018/IJISMD.2020010106

APA

Tiwari, S. (2020). A Blur Classification Approach Using Deep Convolution Neural Network. International Journal of Information System Modeling and Design (IJISMD), 11(1), 93-111. http://doi.org/10.4018/IJISMD.2020010106

Chicago

Tiwari, Shamik. "A Blur Classification Approach Using Deep Convolution Neural Network," International Journal of Information System Modeling and Design (IJISMD) 11, no.1: 93-111. http://doi.org/10.4018/IJISMD.2020010106

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Abstract

Computer vision-based gesture identification is designed to recognize human actions with the help of images. During the process of gesture image acquisition, images suffer various degradations. The method of recovering these degraded images is called restoration. In the case of blind restoration of such a degraded image where blur information is unavailable, it is essential to determine the exact blur type. This article presents a convolution neural network model for blur classification which categories a blur found in a hand gesture image into one of the four blur categories: motion, defocus, Gaussian, and box blur. The simulation results demonstrate the improved preciseness of the CNN model when compared to the MLP model.

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