Noise Reduction of High Density Impulse Noise using First Order Neighborhood Mean Filter
Sagar Chouksey, Dushyant Verma, Ashutosh Chouksey · 2014
In this paper, a new algorithm has been proposed for the restoration of gray scale and color images which get highly corrupted by fixed value impulse noise (salt and pepper). This paper is designed to get better result at high density noise in corrupted images. There are two steps in the proposed algorithm for de-noising the image first step is to detect that the pixel is corrupted or not and the second step is to replace the pixel if it is corrupted by mean of its neighborhood pixels. The proposed algorithm considers first order neighborhood pixels for detecting the noisy pixel and mean filter is considered. Proposed algorithm is compared with all other standard and well known algorithms and found to have better result at high noise densities i.e. 80-90%. The proposed algorithm shows better results than Median Filter (MF), Adaptive Median Filter (AMF), Progressive Switched Median Filter (PSMF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF), and Modified Non-Linear Filter (MNF). Different grayscale and color images are tested by using the algorithm and it gave better Peak Signal Noise Ratio (PSNR) and Image Enhancement Factor (IEF) at low, medium and high noise densities. Keyword- Salt and Pepper (SNP), Mean Filter (MF), Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF)