@inproceedings{fedcfcaec4f04414822aecf44da689d9,
title = "Learning a wavelet tree for multichannel image denoising",
abstract = "We propose a new multichannel image denoising algorithm. To exploit important inter-channel dependencies, we first use dynamic programming to learn an explicit dyadic tree representation of the common structure of the channels. Based on this dyadic tree, optimal Haar wavelet thresholding is then applied to denoise the image. In addition to the original channels, the algorithm can employ multiple derived channels to improve tree learning. Experimental results confirm that the approach improves multichannel image denoising performance both in PSNR and in edge preservation.",
keywords = "Wavelet transforms, dynamic programming, image enhancement, signal denoising",
author = "Xiang, {Zhen James} and Zhuo Zhang and Pingmei Xu and Ramadge, {Peter J.}",
year = "2011",
doi = "10.1109/ICIP.2011.6116187",
language = "English (US)",
isbn = "9781457713033",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "2565--2568",
booktitle = "ICIP 2011",
note = "2011 18th IEEE International Conference on Image Processing, ICIP 2011 ; Conference date: 11-09-2011 Through 14-09-2011",
}