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Related Experiment Video

Updated: Jul 7, 2026

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

Tri-state median filter for image denoising.

T Chen¹, K K Ma, L H Chen

¹School of Computer Science and Software Engineering, Monash University, Clayton Campus, Vic. 3168, Australia. tchen@cs.monash.edu.au

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

|February 13, 2008

Summary

This summary is machine-generated.

A new tri-state median (TSM) filter effectively removes impulse noise from images while preserving details. This novel approach outperforms standard median filters in noise reduction and image quality.

Related Experiment Videos

Last Updated: Jul 7, 2026

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

Area of Science:

Image processing
Digital signal processing
Computer vision

Background:

Impulse noise significantly degrades image quality.
Existing median filters struggle to balance noise suppression and detail preservation.
Need for advanced filtering techniques in image restoration.

Purpose of the Study:

To introduce a novel nonlinear filter, the tri-state median (TSM) filter.
To enhance image detail preservation during impulse noise suppression.
To improve upon the performance of existing median filters.

Main Methods:

Developed a noise detection framework to identify corrupted pixels.
Integrated standard median (SM) and center weighted median (CWM) filters within the framework.
Applied filtering based on noise detection to preserve image integrity.

Main Results:

The TSM filter demonstrated superior performance in extensive simulations.
Achieved a better balance between noise reduction and detail preservation compared to other median filters.
Effectively suppressed impulse noise while retaining important image features.

Conclusions:

The proposed TSM filter is a highly effective method for impulse noise removal.
TSM filter offers significant advantages in maintaining image quality and details.
This filter represents a valuable advancement in image denoising techniques.