As an emerging paradigm for signal acquisition and reconstruction, compressive sensing
(CS) achieves high-speed sampling and compression jointly and has found its way into …

This paper considers the use of a machine learning system for the reconstruction and
recognition of distorted or damaged patterns, in particular, images of faces partially covered …

We present a gradient-based heuristic method for supervised learning of sparsity-promoting
regularizers for denoising signals and images. Sparsity-promoting regularization is a key …

There is recent interest in techniques to accelerate the data acquisition process in MRI by
acquiring limited measurements. Sophisticated reconstruction algorithms are often deployed …

Purpose The recently proposed sparsifying transform (ST) models incur low computational
cost and have been applied to medical imaging. Meanwhile, deep models with nested …

Image recognition and reconstruction are common problems in many image processing
systems. These problems can be formulated as a solution to the linear inverse problem. This …

Magnetic resonance imaging (MRI) in medical imaging plays a vital role in the clinical
diagnostic. The motivation behind reconstruction of MRI is to reduce the radiation exposure …

Traditional model-based image reconstruction (MBIR) methods combine forward and noise
models with simple object priors. Recent application of deep learning methods for image …