A Novel Directional Weighted Minimum Deviation (DWMD) Based Filter for Removal of Random Valued Impulse Noise

The most median-based de noising methods works fine for restoring the images corrupted by Randomn Valued Impulse Noise with low noise level but very poor with highly corrupted images. In this paper a directional weighted minimum deviation (DWMD) based filter has been proposed for removal of high random valued impulse noise (RVIN). The proposed approach based on Standard Deviation (SD) works in two phases. The first phase detects the contaminated pixels by differencing between the test pixel and its neighbor pixels aligned with four main directions. The second phase filters only those pixels keeping others intact. The filtering scheme is based on minimum standard deviation of the four directional pixels. Extensive simulations show that the proposed filter not only provide better performance of de noising RVIN but can preserve more details features even thin lines or dots. This technique shows better performance in terms of PSNR, Image Fidelity and Computational Cost compared to the existing filters.

Image Filtering using All Neighbor Directional Weighted Pixels: Optimization using Particle Swarm Optimization

In this paper a novel approach for de noising images corrupted by random valued impulses has been proposed. Noise suppression is done in two steps. The detection of noisy pixels is done using all neighbor directional weighted pixels (ANDWP) in the 5 x 5 window. The filtering scheme is based on minimum variance of the four directional pixels. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) has also been used for searching the parameters of detection and filtering operators required for optimal performance. Results obtained shows better de noising and preservation of fine details for highly corrupted images.