2 research outputs found
High Density Noise Removal by Cascading Algorithms
An advanced non-linear cascading filter algorithm for the removal of high
density salt and pepper noise from the digital images is proposed. The proposed
method consists of two stages. The first stage Decision base Median Filter
(DMF) acts as the preliminary noise removal algorithm. The second stage is
either Modified Decision Base Partial Trimmed Global Mean Filter (MDBPTGMF) or
Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) which is
used to remove the remaining noise and enhance the image quality. The DMF
algorithm performs well at low noise density but it fails to remove the noise
at medium and high level. The MDBPTGMF and MDUTMF have excellent performance at
low, medium and high noise density but these reduce the image quality and blur
the image at high noise level. So the basic idea behind this paper is to
combine the advantages of the filters used in both the stages to remove the
Salt and Pepper noise and enhance the image quality at all the noise density
level. The proposed method is tested against different gray scale images and it
gives better Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR) and
Image Enhancement Factor (IEF) than the Adaptive Median Filter (AMF), Decision
Base Unsymmetric Trimmed Median Filter (DBUTMF), Modified Decision Base
Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision Base Partial Trimmed
Global Mean Filter (DBPTGMF).Comment: 6 pages, 6 figure