Department of Mathematics
Compression and denoising using l 0-norm
In this paper, we deal with l 0-norm data fitting and total variation regularization for image compression and denoising. The l 0-norm data fitting is used for measuring the number of non-zero wavelet coefficients to be employed to represent an image. The regularization term given by the total variation is to recover image edges. Due to intensive numerical computation of using l 0-norm, it is usually approximated by other functions such as the l 1-norm in many image processing applications. The main goal of this paper is to develop a fast and effective algorithm to solve the l 0-norm data fitting and total variation minimization problem. Our idea is to apply an alternating minimization technique to solve this problem, and employ a graph-cuts algorithm to solve the subproblem related to the total variation minimization. Numerical examples in image compression and denoising are given to demonstrate the effectiveness of the proposed algorithm. © 2010 Springer Science+Business Media, LLC.
Source Publication Title
Computational Optimization and Applications
Link to Publisher's Edition
Yau, A., Tai, X., & Ng, M. (2011). Compression and denoising using l 0-norm. Computational Optimization and Applications, 50 (2), 425-444. https://doi.org/10.1007/s10589-010-9352-4