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Related Experiment Video

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Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

Fast impulsive noise removal.

¹Institute for Simulation and Training, University of Central Florida, Orlando, FL 32826-0544, USA. pwindyga@ist.ucf.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 6, 2008

Summary

A new recursive nonlinear filter efficiently removes impulsive noise from images. This novel filter preserves image data and histogram range while being faster than the median filter.

Area of Science:

Digital Image Processing
Signal Processing
Computer Vision

Background:

Impulsive noise, often called salt-and-pepper noise, degrades image quality.
Existing filters like the median filter are effective but can be computationally intensive.
Efficient noise reduction is crucial for subsequent image analysis tasks.

Purpose of the Study:

To introduce a novel n-dimensional recursive nonlinear filter for impulsive noise elimination.
To evaluate the performance of this new filter against the standard median filter.
To explore its applicability in preliminary image processing for noise removal.

Main Methods:

A recursive nonlinear filter employing two independent conditional rules.
Identification of noisy pixels by neighborhood inspection.

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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

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Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

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Replacement of noisy pixel values with conservative neighbor values, conserving histogram distribution.

Decomposition into lower-dimensional filters for implementation, focusing on 2D signals (images).

Main Results:

The proposed filter demonstrates comparable performance to the median filter in noise reduction and information conservation.
The new filter is significantly faster than the median filter.
Effective in reducing noise in both synthetic and real images, with a slight smoothing effect on non-noisy regions.

Conclusions:

The developed n-dimensional filter offers a faster and effective alternative to the median filter for impulsive noise reduction.
Its speed and comparable performance suggest its suitability as a replacement for median filtering in advanced noise removal pipelines.
The filter is scalable, easily implemented, and adaptable for various applications.