Noise Removal using Chebyshev Functional Link Artificial Neural Network with Backpropagation

Abstract

In this proposed work, we have used an alternate ANN structure called Functional link ANN for image denoising. In contrast to other feed forward neural networks, the FLANN is a single layer structure, which never contain any hidden layer and non-linearity is introduced by enhancing the input pattern with a nonlinear function expansion called Chebyshev functional expansion. With the Chebyshev functional expansion, the network shows very good result in denoising the image corrupted by four different noise called Salt and Pepper noise, Gaussian noise, Speckle noise and Poisson noise. In this paper Gaussian noise is added to the speckle noise to give better result. In particular FLANN structure with Chebyshev functional expansion works best for Poisson noise suppression from an image. Here Back Propagation network is used to train the Chebyshev expanded image. BP network can be used to learn and store a great deal of mapping relations of input-output models, and no need to disclose in advance the mathematical equation that describes these mapping relations. Feed Forward Back Propagation (FFBP) algorithm performs quite well in the presence of different noise while preserving the image features satisfactorily