Ultrasound image restoration based on a learned dictionary and a higher-order MRF
Ultrasound image restoration based on a learned dictionary and a higher-order MRF
复制标题
基于学习字典和高阶 MRF 的超声图像恢复
DOI：
10.1016/j.camwa.2018.10.031
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发表时间：
2019-02-15
影响因子：
2.9
通讯作者：
Zou, Yuru
中科院分区：
文献类型：
--
作者：
Lu, Jian;Yang, Hanmei;Zou, Yuru
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. (C) 2018 Elsevier Ltd. All rights reserved.
