An image decomposition parallel approach to train flann for an adaptive filter

Abstract

Filtering method is applied to the images corrupted at the time of transmission due to several noises, with varying strengths and different noise probability. Neural network based image filter is one of the most important example of adaptive image filter. Adaptive neural network filter remove various types of noise such as Gaussian noise and impulsive noise. Neural networks are based on the concept of training or learning by examples and have already been applied in several domains of image processing including image filtering. But training of those neural networks consume much time before it is actually tested on such as image filtering. Applying parallelism to image processing is increasingly practical and necessary, as our desktops are becoming multicore machines replacing single core. Therefore, this paper proposes a parallel approach named image decomposition parallel approach to train FLANN (Functional Link Artificial Neural Network). Well trained FLANN is used for rectifying the corrupted pixels to restore the image. Experimental results obtained through SPMD(Single Program Multiple Data) simulation environment show that the proposed parallel approach to train the FLANN is feasible as it substantially reduces the training period and also make it an efficient filter to restore the image fairly well maintaining the quality of the filtered image. Hence, this method is suitable for real time image restoration applications. © 2011 Springer-Verlag Berlin Heidelberg.