Title :
Image restoration using L1-norm regularization and a gradient-based neural network with discontinuous activation functions
Author :
Ferreira, Leonardo V. ; Kaszkurewicz, Eugenius ; Bhaya, Amit
Author_Institution :
Dept. of Electr. Eng., Fed. Univ. of Rio de Janeiro, Rio de Janeiro
Abstract :
The problem of restoring images degraded by linear position invariant distortions and noise is solved by means of a L1-norm regularization, which is equivalent to determining a L1-norm solution of an overdetermined system of linear equations, which results from a data-fitting term plus a regularization term that are both in L1 norm. This system is solved by means of a gradient-based neural network with a discontinuous activation function, which is ensured to converge to a L1-norm solution of the corresponding system of linear equations.
Keywords :
gradient methods; image restoration; neural nets; L1-norm regularization; data fitting; discontinuous activation function; gradient-based neural network; image restoration; linear position invariant distortion; noise; Degradation; Equations; Focusing; Helium; Image restoration; Image sensors; Inverse problems; Neural networks; Optical distortion; Predistortion;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634149
