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Related Experiment Video

Updated: May 28, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Spatially adapted total variation model to remove multiplicative noise.

Dai-Qiang Chen¹, Li-Zhi Cheng

¹Department of Mathematics and System, School of Sciences, National University of Defense Technology, Changsha, China. chener050@sina.com

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

|October 25, 2011

Summary

This study introduces a new variational model for multiplicative noise removal in images. The method effectively preserves image details while removing noise, outperforming current state-of-the-art techniques.

Related Experiment Videos

Last Updated: May 28, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Image Science
Computational Imaging
Applied Mathematics

Background:

Total Variation (TV) regularization is a common method for removing multiplicative noise in images.
Existing methods often struggle to balance noise removal with detail preservation.

Purpose of the Study:

To develop an advanced variational model for multiplicative noise removal.
To improve image denoising by preserving fine details and enhancing noise reduction in homogeneous regions.

Main Methods:

A novel variational model combining TV regularization with local constraints.
Spatially adapted regularization parameters selected based on local statistical characteristics.
Efficient solution using the augmented Lagrangian method.

Main Results:

The proposed algorithm successfully preserves small image details.
Effective noise removal in homogeneous image regions.
Demonstrated superior denoising performance compared to state-of-the-art methods, evidenced by improved signal-to-noise ratio (SNR) values.

Conclusions:

The developed variational model offers enhanced multiplicative noise removal.
The method provides a robust approach for preserving image fidelity.
This technique represents a significant advancement in image denoising.