Title :
Defocus blur estimation using a Cellular Neural Network
Author :
Jongsu Lee ; Fathi, A.S. ; Sangseob Song
Author_Institution :
Dept. of Electron. Eng., Chonbuk Nat. Univ., Jeonju, South Korea
Abstract :
Blur identification is one of the most important parts of digital image processing. A conventional blur that occurs in images is out of focus, which is generated because of lens defocus. In this sense, many researchers have presented methods to estimate defocus blur so far. This paper shows how the Cellular Neural Network (CNN) can be used to estimate defocus blur parameter. After the point spread function (PSF) parameter is obtained by the CNN, defocused blur images can be restored effectively.
Keywords :
cellular neural nets; image restoration; cellular neural network; defocus blur estimation; digital image processing; image restoration; point spread function parameter; Cellular networks; Cellular neural networks; Digital images; Focusing; Frequency; Image edge detection; Image restoration; Optical noise; Parameter estimation; Radial basis function networks; blur identification; cellular neural network; defocus;
Conference_Titel :
Cellular Nanoscale Networks and Their Applications (CNNA), 2010 12th International Workshop on
Conference_Location :
Berkeley, CA
Print_ISBN :
978-1-4244-6679-5
DOI :
10.1109/CNNA.2010.5430249