@article{5dbdc5d3730f442cab4197f97afe1306,
title = "On semismooth Newton's methods for total variation minimization",
abstract = "In [2], Chambolle proposed an algorithm for minimizing the total variation of an image. In this short note, based on the theory on semismooth operators, we study semismooth Newton's methods for total variation minimization. The convergence and numerical results are also presented to show the effectiveness of the proposed algorithms.",
keywords = "Denoising, Regularization, Semismooth Newton's methods, Total variation",
author = "Ng, {Michael K.} and Liqun Qi and Yang, {Yu Fei} and Huang, {Yu Mei}",
note = "Funding Information: ∗The research of this author is supported in part by Hong Kong Research Grants Council Grant Nos. 7035/04P and 7035/05P, and HKBU FRGs. †The research of this author is supported in part by the Research Grant Council of Hong Kong. ‡This work was started while the author was visiting Department of Applied Mathematics, The Hong Kong Polytechnic University. The research of this author is supported in part by The Hong Kong Polytechnic University Postdoctoral Fellowship Scheme and the National Science Foundation of China (No. 60572114).",
year = "2007",
month = apr,
doi = "10.1007/s10851-007-0650-0",
language = "English",
volume = "27",
pages = "265--276",
journal = "Journal of Mathematical Imaging and Vision",
issn = "0924-9907",
publisher = "Springer Netherlands",
number = "3",
}