@inproceedings{9f11dd3cdcca4d11bd11b871991a56b5,
title = "Accurate prior modeling in the locally adaptive window-based wavelet denoising",
abstract = "The locally adaptive window-based (LAW) denoising method has been extensively studied in literature for its simplicity and effectiveness. However, our statistical analysis performed on its prior estimation reveals that the prior is not estimated properly. In this paper, a novel maximum likelihood prior modeling method is proposed for better characterization of the local variance distribution. Goodness of fit results shows that our proposed prior estimation method can improve the model accuracy. A modified LAW denoising algorithm is then proposed based on the new prior. Image denoising experimental results demonstrate that the proposed method can significantly improve the performance in terms of both peak signal-to noise ratio (PSNR) and visual quality, while maintain a low computation.",
keywords = "Adaptive parameter estimation, Image denoising, Maximum likelihood estimation, Orthogonal wavelet transform, Visual quality",
author = "Liu, {Yun Xia} and Yang Yang and Law, {Ngai Fong}",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-42294-7_47",
language = "English",
isbn = "9783319422930",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "523--533",
booktitle = "Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings",
address = "Germany",
note = "12th International Conference on Intelligent Computing Theories and Application, ICIC 2016 ; Conference date: 02-08-2016 Through 05-08-2016",
}