An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

Abstract

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

“…Some researchers have suggested the use of artificial intelligence (AI) in reducing impulse noise in digital images. Researchers have utilized fuzzy approaches [13][14][15][16], artificial neural networks (ANNs) [17][18][19], and support vector machines (SVMs) [20] for reducing the impulse noise levels in digital images. However, AI-based approaches normally have high complexity, are complicated as they involve many parameters, and require longer processing time.…”