Image fusion using Bi-dimensional Empirical Mode Decomposition for salt and pepper noise removal in digital images

Abstract

The digital images are corrupted by impulse noise due to errors generated in camera sensors, analog-to-digital conversion and communication channels. Therefore it is necessary to remove impulse noise in-order to provide further processing such as edge detection, segmentation, pattern recognition etc. Filtering a noisy image, while preserving the image details is one of the most important issues in image processing. In this paper, introduces an image fusion technique for impulse noise reduction, where the fused image will combine the uncorrupted pixels of the noisy images obtained from different sensors. The image captured by different sensors undergoes iterative filtering algorithm, search for the noise-free pixels within a small neighborhood. The noisy pixel is then replaced with the value estimated from the noise-free pixels. The process continues iteratively until all noisy-pixels of the noisy image are filtered. The filtered images are fused in to a single image using a fusion algorithm by the Bi-dimensional Empirical Mode Decomposition (BEMD). The experimental results show the proposed algorithm can perform significantly better in terms of noise suppression and detail preservation in images than a number of existing nonlinear techniques.