A Blur Classification Approach Using Deep Convolution Neural Network

Shamik Tiwari
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Shamik Tiwari: School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

International Journal of Information System Modeling and Design (IJISMD), 2020, vol. 11, issue 1, 93-111

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jismd0:v:11:y:2020:i:1:p:93-111

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