Adaptive intelligence aims at empowering machine learning techniques with the additional
use of domain knowledge. In this work, we present the application of adaptive intelligence to …

There has been a tremendous increase in applications of the inverse problem framework to
parameter estimation in magnetic resonance. Attempting to capture both the basics of this …

In this paper, we study ℓ 1− αℓ 2 (0< α⩽ 1) minimization methods for signal and image
reconstruction with impulsive noise removal. The data fitting term is based on ℓ 1 fidelity …

Moving force identification (MFI) techniques have been widely studied in recent years.
However, the contradiction between response acquisition and energy consumption limits …

Purpose To develop an accelerated, robust, and accurate diffusion MRI acquisition and
reconstruction technique for submillimeter whole human brain in vivo scan on a clinical …

Within the conventional sparse Bayesian learning (SBL) framework, only Gaussian scale
mixtures have been adopted to model sparsity-inducing priors that guarantee the exact …

Compressive sensing (CS) spectroscopy is well known for developing a compact
spectrometer which consists of two parts: compressively measuring an input spectrum and …

The brain represents and reasons probabilistically about complex stimuli and motor actions
using a noisy, spike-based neural code. A key building block for such neural computations …

Compressed sensing in both noiseless, and noisy cases is considered in this article, and
uniform restricted isometry property (RIP) conditions for sparse signal recovery are …

In dictionary learning, sparse regularization is used to promote sparsity and has played a
major role in the developing of dictionary learning algorithms. ℓ 1-norm is of the most …