In recent years, dictionary learning has shown to be an efficient tool in recovering images from their degraded, damaged or incomplete version. Especially, for medical images that contain significant details and characteristics. In this work, the authors are interested in this unsupervised learning technique for discovering and visualising the underlying structure of a medical image. Therefore, an adaptive bi-dictionary learning model for recovering magnetic resonance (MR) image from undersampled measurements is introduced. The proposed model learns two dictionaries, one over the underlying image and the other over its sparse gradient. Hence, the algorithm minimises a linear combination of three terms corresponding to the least-squares data fitting, dictionary learning over the pixel domain, and gradient-based dictionary. Numerically, experimental results on several MR images demonstrate that the proposed bi-dictionary framework can improve reconstruction accuracy over other methods.

中文翻译：

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从欠采样数据重建医学图像的双向学习模型

近年来，字典学习已被证明是从退化，损坏或不完整的版本中恢复图像的有效工具。特别是对于包含重要细节和特征的医学图像。在这项工作中，作者对这种用于发现和可视化医学图像基础结构的无监督学习技术感兴趣。因此，介绍了一种用于从欠采样测量中恢复磁共振（MR）图像的自适应双向学习模型。所提出的模型学习两个字典，一个在基础图像上，另一个在其稀疏梯度上。因此，该算法最小化了与最小二乘数据拟合，像素域上的字典学习以及基于梯度的字典相对应的三个项的线性组合。在数值上