Compressed sensing (CS) and its medical applications are active areas of research. In this
paper, we review recent works using deep learning method to solve CS problem for images …

We consider the problem of seismic reflectivity inversion, which pertains to the high-
resolution recovery of interface locations and reflection coefficients from seismic …

We solve the analysis sparse coding problem considering a combination of convex and non-
convex sparsity promoting penalties. The multi-penalty formulation results in an iterative …

This paper studies the external division operator, an external division (an affine combination
with positive and negative weights) of two proximity operators. We show that the external …

We consider the reflection seismology problem of recovering the locations of interfaces and
the amplitudes of reflection coefficients from seismic data, which are vital for estimating the …

The choice of the sensing matrix is crucial in compressed sensing. Random Gaussian
sensing matrices satisfy the restricted isometry property, which is crucial for solving the …

Finite-rate-of-innovation (FRI) signal model is well suited for time-of-flight imaging
applications such as ultrasound, lidar, sonar, radar, and more. Due to their finite degrees of …

We solve the problem of sparse signal deconvolution in the context of seismic reflectivity
inversion, which pertains to high-resolution recovery of the subsurface reflection coefficients …

We address the problem of recovering signals from compressed measurements based on
generative priors. Recently, generative-model based compressive sensing (GMCS) methods …

The choice of the sensing matrix is crucial in compressed sensing (CS). Gaussian sensing
matrices possess the desirable restricted isometry property (RIP), which is crucial for …