Objective: We propose a method for the reconstruction of parameter-maps in Quantitative
Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter …

Abstract Background Unrolled neural networks (NNs) have been extensively applied to
different image reconstruction problems across all imaging modalities. A key component of …

Purpose In the past, dictionary learning (DL) and sparse coding (SC) have been proposed
for the regularization of image reconstruction problems. The regularization is given by a …

This paper studies the performance of OMP in comparison to Thresholding in the case in
which only a perturbed version of the generating dictionary is known. Both theory and …

Finding the size of the dictionary is an open issue in dictionary learning (DL). We propose an
algorithm that adapts the size during the learning process by using Information Theoretic …

In the last years, the design of image reconstruction methods in the field of quantitative
Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when …

In this paper we derive sufficient conditions for the convergence of two popular alternating
minimisation algorithms for dictionary learning-the Method of Optimal Directions (MOD) and …

针对欠定盲语音分离传统字典学习算法不能优化字典尺寸的问题, 提出了一种尺度自适应同步码
字优化(Scale Adaptive Simultaneous Codeword Optimization, SASimCO) 算法 …

We propose a dictionary learning (DL) algorithm for signals in additive noise with
generalized Gaussian distribution (GGD) by redesigning three key components used in DL …

One of the key findings on which many signal processing tasks are built is that even high-
dimensional signals admit some kind of sparse representation in a suitable generator …