基于组稀疏表示的医学图像超分辨率重建

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 151-153.

基于组稀疏表示的医学图像超分辨率重建

黄浩锋,肖南峰

华南理工大学计算机科学与工程学院广州510006,华南理工大学计算机科学与工程学院广州510006

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金资助

Super-resolution Reconstruction of Medical Images Based on Group Sparse Representation

HUANG Hao-feng and XIAO Nan-feng

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 在大量的医学图像处理过程中,由于现有的硬件设备和成像技术的限制,还不能够获取满足高要求的清晰图像。因此,在现有的硬件设备和成像技术下获取的医学图像需要进行超分辨率的重建处理。在基于稀疏表示单帧图像超分辨的基础上,针对医学图像具有明显的重复结构等特点,提出了一种基于组稀疏的单帧医学图像超分辨算法。并且结合Group Lasso算法和K-SVD算法,提出了一种新的字典训练算法。实验结果分析和比较证实提出的算法在性能指标上比现有的其它几种方法均有所提高。

Abstract: Medical diagnosis needs a lot of medical image processing.Due to the technological and economical limits,the medical diagnosis is not able to get the clear medical images.Therefore,it is necessary to reconstruct the medical images with super-resolution methods.Based on super-resolution reconstruction of single image by the sparse coding,and considering that there are obviously repetitive image structures in the medical images,this paper proposed a reconstruction method for the super-resolution medical images based on the group sparse representation.In addition,this paper also presented an dictionary train algorithm which combines the Group Lasso with K-SVD.The experimental results indicate that the proposed algorithms have higher performance than that of the existing methods.

Key words: Medical image,Super-resolution reconstruction,Dictionary,Group sparse

黄浩锋,肖南峰. 基于组稀疏表示的医学图像超分辨率重建[J]. 计算机科学, 2015, 42(Z6): 151-153. https://doi.org/

HUANG Hao-feng and XIAO Nan-feng. Super-resolution Reconstruction of Medical Images Based on Group Sparse Representation[J]. Computer Science, 2015, 42(Z6): 151-153. https://doi.org/

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