Noise removal using First Order Neighborhood Mean Filter
Priyanka Shrivastava, Uday Pratap Singh · 2014
A numbers of algorithms have been proposed for the removal of fixed valued impulse noise (salt and pepper) from highly corrupted gray scale and color images, but they failed to give better results at high noise densities. This paper is designed in accordance with the proposed algorithm to get better result at high noise density levels. The proposed algorithm works on two steps for de-noising the image first step is to detect that the pixel is noisy or not and the second step is the replacement of that noisy pixel. The proposed algorithm considers first order neighborhood pixels for detecting the noisy pixel and mean filter is considered. Color images are also de-noised by extracting the R, G and B pixels from noisy image and then they are de-noised separately and then merged together to again form the color image. Proposed algorithm is compared with all other standard and well known algorithms and found to have good noise removal capabilities at high densities. This 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.