The nature of statistical learning theory
The nature of statistical learning theory
Digital Image Processing
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Impulse noise removal by multi-state median filtering
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming
International Journal of Imaging Systems and Technology
A particle swarm optimization based algorithm to the Internet subscription problem
Expert Systems with Applications: An International Journal
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
Automatic circle detection on digital images with an adaptive bacterial foraging algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A new efficient approach for the removal of impulse noise from highly corrupted images
IEEE Transactions on Image Processing
Tri-state median filter for image denoising
IEEE Transactions on Image Processing
Selective removal of impulse noise based on homogeneity level information
IEEE Transactions on Image Processing
Universal Impulse Noise Filter Based on Genetic Programming
IEEE Transactions on Image Processing
Partition-based fuzzy median filter based on adaptive resonance theory
Computer Standards & Interfaces
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The paper presents a system using Decision tree, Particle swarm optimization, and Support vector regression to design a Median-type filter with a 2-level impulse detector for image enhancement, called DPSM filter. First, it employs a varying 2-level hybrid impulse noise detector (IND) to determine whether a pixel is contaminated by impulse noises or not. The 2-level IND is constructed by a decision tree (DT) which is built via combining 10 impulse noise detectors. Also, the particle swarm optimization (PSO) algorithm is exploited to optimize the DT. Subsequently, the DPSM filter utilizes the median-type filter with the support vector regression (MTSVR) to restore the corrupted pixels. Experimental results demonstrate that the DPSM filter achieves high performance for detecting and restoring impulse noises, and also outperforms the existing well-known methods under consideration in the paper.