Image Restoration Using M-Flann

Abstract

In this paper a Modified Functional Link Artificial. Neural Network (M-FLANN) is proposed which is simpler than a Multilayer Perceptron (MLP). It have been implemented for image restoration in this paper. Its computational complexity and speed and generalization ability to cancel Gaussian noise is compared with that of MLP. In contrast to a feed forward ANN structure i.e. a multiplayer perceptron (MLP) the M-FLANN is basically a single layer structure in which non-linearity is introduced by enhancing the input pattern with nonlinear function expansion. With the proper choice of functional expansion in a FLANN problem of denoising of an image. In the single layer functional link ANN (FLANN) the need of hidden layer is eliminated.The novelty of the FLANN structure is that it requires much less computation than that of MLP. In the presence of additive white Gaussian noise, salt and pepper noise, Random variable impulse noise and mixed noise in the image the performance of the proposed network is compared with that of MLP in this thesis. The Performance of the of algorithm is evaluated for six different situations i.e. for single layer neural network, MLP and four different types of expansion in FLANN and comparison in terms of computational complexity also carried out.