Blur Classification using Ridgelet Transform and Feed Forward Neural Network
Автор: Shamik Tiwari, V. P. Shukla, S. R. Biradar, A. K. Singh
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 9 vol.6, 2014 года.
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The objective of image restoration approach is to recover a true image from a degraded version. This problem can be stated as blind or non-blind depending upon whether blur parameters are known prior to the restoration process. Blind restoration deals with parameter identification before deconvolution. Though there exists multiple blind restorations techniques but blur type recognition is extremely desirable before application of any blur parameters estimation approach. In this paper, we develop a blur classification approach that deploys a feed forward neural network to categories motion, defocus and combined blur types. The features deployed for designing of classification system include mean and standard deviation of ridgelet energies. Our simulation results show the preciseness of proposed method.
Blur classification, Motion blur, Defocus blur, Ridgelet Transform, Neural Network
Короткий адрес: https://sciup.org/15013410
IDR: 15013410
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