Feed Forward Back Propagation Algorithm for Eliminating Uniform Noise and Impulse Noise

Digital Images are contaminated by noise during acquisition and/or transmission over communication channel. Eliminating impulse noise and uniform noise from the images without damaging their boundaries and fine details is an important and a challenging task in the image processing applications. A nonlinear technique based on decision mechanism for suppressing impulse noise and uniform noise from the images contaminated by low levels of noise is proposed in this paper. The proposed algorithm, called, Feed Forward back Propagation (FFBP) algorithm performs quite well in the presence of the long-tailed impulse noise and short tailed uniform noise while preserving the image features satisfactorily. The proposed intelligent algorithm is carried out in two stages. In first stage the corrupted image is filtered by applying a decision based nonlinear filter. This decision based nonlinear filtered output images and noisy images are suitably combined with a feed forward neural network in the second stage. The parameters of the feed forward neural network are adaptively optimized by training of three well known images. This is quite efficient in eliminating uniform noise and impulse noise. Simulation results show that the proposed filter is superior in terms of eliminating impulse noise as well as preserving edges and fine details of digital images and results are compared with other existing nonlinear filters. The performance of the filtering technique has been evaluated by applying it on several test images corrupted by different levels of impulse noise and the results obtained are presented and compared with that of the existing filtering techniques.