Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal
algorithms, for example, the alternating direction method of multipliers (ADMM), with …

To solve inverse problems, plug-and-play (PnP) methods replace the proximal step in a
convex optimization algorithm with a call to an application-specific denoiser, often …

Acoustic source reconstruction techniques are an essential technical basis for noise source
identification and fault diagnosis. How to computationally efficiently obtain the accurate …

Convolutional neural networks (CNN) have emerged as a powerful tool for solving
computational imaging reconstruction problems. However, CNNs are generally difficult-to …

Sparse signal recovery consists of employing a sparsity promoting regularizer to estimate
the underlying signal from an incomplete set of measurements. Typical recovery approaches …

Magnetic Resonance Imaging (MRI) has excellent soft tissue contrast but is hindered by an
inherently slow data acquisition process. Compressed sensing, which reconstructs sparse …

For image recovery problems, plug-and-play (PnP) methods have been developed that
replace the proximal step in an optimization algorithm with a call to an application-specific …

Magnetic Resonance Imaging (MRI) is a non-invasive, non-ionising imaging modality with
unrivalled soft tissue contrast. A key consideration for MRI is data acquisition time, which is …

The learned denoising-based approximate message passing (LDAMP) algorithm has
attracted great attention for image compressed sensing (CS) tasks. However, it has two …

In one approach, VDAMP is improved to allow multiple coils. The aliasing is modeled in the
wavelet domain with spatial modulation for each of the frequency subbands. The spatial …