Improved total variation algorithms for wavelet-based 
denoising

Glenn R. Easley; Flavia Colonna

doi:10.1117/12.717457

9 April 2007 Improved total variation algorithms for wavelet-based denoising

Glenn R. Easley; Flavia Colonna;

Author Affiliations +

Proceedings Volume 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V; 65760J (2007) https://doi.org/10.1117/12.717457
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Many improvements of wavelet-based restoration techniques suggest the use of the total variation (TV) algorithm. The concept of combining wavelet and total variation methods seems effective but the reasons for the success of this combination have been so far poorly understood. We propose a variation of the total variation method designed to avoid artifacts such as oil painting effects and is more suited than the standard TV techniques to be implemented with wavelet-based estimates. We then illustrate the effectiveness of this new TV-based method using some of the latest wavelet transforms such as contourlets and shearlets.

Citation Download Citation

Glenn R. Easley, Flavia Colonna, "Improved total variation algorithms for wavelet-based denoising", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760J (9 April 2007); doi: 10.1117/12.717457; https://doi.org/10.1117/12.717457

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