An adaptive median filter algorithm based on B-spline function

Authors:
Mei-Sen Pan;Jing-Tian Tang;Xiao-Li Yang
Affiliations:
College of Computer Science and Technology, Hunan University of Arts and Science, Changde, PRC 415000 and Institute of Biomedical Engineering, School of Info-physics and Geomatics Engineering, Cen ...;Institute of Biomedical Engineering, School of Info-physics and Geomatics Engineering, Central South University, Changsha, PRC 410083;Institute of Biomedical Engineering, School of Info-physics and Geomatics Engineering, Central South University, Changsha, PRC 410083
Venue:
International Journal of Automation and Computing
Year:
2011

Citing 7
Cited 1

Suppression of Impulse Noise in Medical Images with the Use of Fuzzy Adaptive Median Filter

Journal of Medical Systems
Review: A review on blind detection for image steganography

Signal Processing
An Improved Decision-Based Algorithm for Impulse Noise Removal

CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 1 - Volume 01
Fourth-order partial differential equations for noise removal

IEEE Transactions on Image Processing
Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

IEEE Transactions on Image Processing
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

IEEE Transactions on Image Processing
A universal noise removal algorithm with an impulse detector

IEEE Transactions on Image Processing

L-priorities bloom filter: A new member of the bloom filter family

International Journal of Automation and Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

According to the B-spline convolution mask, first, the contrast sensitiveness (CS) is computed and then is viewed as a noise sensitiveness coefficient (NSC) to adaptively determine a noise-recognized threshold value. Based on the noise density function (NDF) in a 3脳3 window, the filtering window size is adaptively adjusted, and then a median filter is used to eliminate the noise-marked pixels. The experiment results show that the proposed algorithm can preserve image detail information well and effectively remove the noises, particularly the impulse noises that is also called salt-and-pepper noises superimposed on the computed tomography (CT) and magnetic resonance imaging (MRI) medical images.