@article{eeb66f06e8a24411a89a487cf1131a39,
title = "Ultrasound image restoration based on a learned dictionary and a higher-order MRF",
abstract = "The restoration of images degraded by blur and multiplicative noise is a critical preprocessing step in medical ultrasound images which exhibit clinical diagnostic features of interest. This paper proposes a novel non-smooth non-convex variational model for ultrasound images denoising and deblurring motivated by the successes of sparse representation of images and FoE based approaches. Dictionaries are well adapted to textures and extended to arbitrary image sizes by defining a global image prior, while FoE image prior explicitly characterizes the statistics properties of natural image. Following these ideas, the new model is composed of the data-fidelity term, the sparse and redundant representations via learned dictionaries, and the FoE image prior model. The iPiano algorithm can efficiently deal with this optimization problem. The new proposed model is applied to several simulated images and real ultrasound images. The experimental results of denoising and deblurring show that proposed method gives a better visual effect by efficiently removing noise and preserving details well compared with two state-of-the-art methods.",
keywords = "Deblurring, Denoising, FoE image prior, Sparse representation, iPiano algorithm",
author = "Jian Lu and Hanmei Yang and Lixin Shen and Yuru Zou",
note = "Funding Information: The authors would like to thank Dr. Yunjin Chen and Dr. Zhengmeng Jin for kindly sharing original software of their papers. This work was supported in part by the National Natural Science Foundation of China, under Grant 11871348, Grant 61373087, and Grant 61872429, by the Guangdong Natural Science Foundation of China under Grant 2015A030313557 and Grant 2015A030313550, by the China Scholarship Council, by the Specialized Research Fund for the Doctoral Program of Chinese Higher Education under Grant 20134408110001, by the HD Video R & D Platform for Intelligent Analysis and Processing in Guangdong Engineering Technology Research Centre of Colleges and Universities (No. GCZX-A1409), and by Natural Science Foundation of Shenzhen under Grant JCYJ20170818091621856. Funding Information: The authors would like to thank Dr. Yunjin Chen and Dr. Zhengmeng Jin for kindly sharing original software of their papers. This work was supported in part by the National Natural Science Foundation of China , under Grant 11871348 , Grant 61373087 , and Grant 61872429 , by the Guangdong Natural Science Foundation of China under Grant 2015A030313557 and Grant 2015A030313550, by the China Scholarship Council , by the Specialized Research Fund for the Doctoral Program of Chinese Higher Education under Grant 20134408110001 , by the HD Video R & D Platform for Intelligent Analysis and Processing in Guangdong Engineering Technology Research Centre of Colleges and Universities (No. GCZX-A1409 ), and by Natural Science Foundation of Shenzhen under Grant JCYJ20170818091621856 . Publisher Copyright: {\textcopyright} 2018 Elsevier Ltd",
year = "2019",
month = feb,
day = "15",
doi = "10.1016/j.camwa.2018.10.031",
language = "English (US)",
volume = "77",
pages = "991--1009",
journal = "Computers and Mathematics with Applications",
issn = "0898-1221",
publisher = "Elsevier",
number = "4",
}