@Article{CSIAM-AM-1-2,
author = {Yiming, Gao and Jie, Huang and Xu, Li and Liu, Hairong and Yang, Xiaoping},
title = {An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images},
journal = {CSIAM Transactions on Applied Mathematics},
year = {2020},
volume = {1},
number = {2},
pages = {256--276},
abstract = {<p style="text-align: justify;">A good statistical model of speckle formation is useful to design a good
speckle reduction model for clinical ultrasound images. We propose a new general distribution to describe the distribution of speckle in clinical ultrasound images according to a log-compression algorithm of clinical ultrasound imaging. A new variational
model is designed to remove the speckle noise with the proposed general distribution.
The efficiency of the proposed model is confirmed by experiments on synthetic images
and real ultrasound images. Compared with previous variational methods which assign a designated distribution, the proposed method is adaptive to remove different
kinds of speckle noise by estimating parameters to find suitable distribution. The experiments show that the proposed method can adaptively remove different types of
speckle noise.</p>},
issn = {2708-0579},
doi = {https://doi.org/10.4208/csiam-am.2020-0010},
url = {https://global-sci.com/article/82376/an-adaptive-data-fitting-model-for-speckle-reduction-of-log-compressed-ultrasound-images}
}