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Related Experiment Video

Updated: Jun 28, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Efficient total variation minimization methods for color image restoration.

You-Wei Wen¹, Michael K Ng, Yu-Mei Huang

¹Faculty of Science, South China Agricultural University, Wushan, Guangzhou, China. tslwyw@nus.edu.sg

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

|November 1, 2008

Summary

This summary is machine-generated.

This study introduces a new total variation minimization model for restoring color images. The proposed method effectively denoises images and achieves competitive restoration quality compared to existing techniques.

Related Experiment Videos

Last Updated: Jun 28, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

Computer Vision
Image Processing
Applied Mathematics

Background:

Color image restoration is crucial for various applications.
Existing methods face challenges in effectively denoising and deblurring images.
Total variation minimization is a promising approach for image restoration.

Purpose of the Study:

To propose and analyze a novel total variation minimization model for color image restoration.
To develop an efficient algorithm for solving the proposed model.
To evaluate the performance of the proposed method against existing techniques.

Main Methods:

A color total variation minimization scheme is utilized for image denoising.
An alternating minimization algorithm is employed to solve the optimization problem.
Convergence analysis of the algorithm is provided.

Main Results:

The alternating minimization algorithm demonstrates convergence.
The algorithm is shown to be computationally efficient.
Experimental results indicate high-quality restored color images.

Conclusions:

The proposed total variation minimization model is effective for color image restoration.
The developed algorithm is efficient and converges reliably.
The method achieves competitive performance with state-of-the-art techniques.