摘要稀疏表示模型常利用训练样本学习过完备字典, 旨在获得信号的冗余稀疏表示. 设计简单,
高效, 通用性强的字典学习算法是目前的主要研究方向之一, 也是信息领域的研究热点 …

The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …

Objective: Improve the reconstructed image with fast and multiclass dictionaries learning
when magnetic resonance imaging is accelerated by undersampling the k-space data …

In recent years, sparse signal modeling, especially using the synthesis model has been
popular. Sparse coding in the synthesis model is however, NP-hard. Recently, interest has …

The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure, while maintaining high image quality is an …

Natural signals and images are well known to be approximately sparse in transform
domains such as wavelets and discrete cosine transform. This property has been heavily …

Many applications in signal processing benefit from the sparsity of signals in a certain
transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapted to …

Sparse representation has shown to be a very powerful model for real world signals, and
has enabled the development of applications with notable performance. Combined with the …

Techniques exploiting the sparsity of signals in a transform domain or dictionary have been
popular in signal processing. Adaptive synthesis dictionaries have been shown to be useful …

Compressed sensing is a powerful tool in applications such as magnetic resonance imaging
(MRI). It enables accurate recovery of images from highly undersampled measurements by …