Functional Link Artificial Neural Network for Denoising of Image

Digital image denoising is crucial part of image preprocessing. The application of denoising procesn satellite image data and also in television broadcasting. Image data sets collected by image sensors are generally contaminated by noise. Furthermore, noise can be introduced by transmission errors and compression. Thus, denoising is often a necessary and the first step to be taken before the images data is analyzed. 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 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 paper. 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. Index Terms— MLP, FLANN, Salt and Pepper noise.

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