In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind
deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown …

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

The dictionary learning problem, representing data as a combination of a few atoms, has
long stood as a popular method for learning representations in statistics and signal …

The problem of sparse multichannel blind deconvolution (S-MBD) arises frequently in many
engineering applications such as radar/sonar/ultrasound imaging. To reduce its …

The algorithm unfolding networks with explainability of algorithms and higher efficiency of
Deep Neural Networks (DNN) have received considerable attention in solving ill-posed …

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 widespread adoption of deep learning to build models that capture the dynamics of
neural populations is typically based on “black-box” approaches that lack an interpretable …

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 …

In a famous Socratic dialogue by Plato, Meno postulates that the epistemic pursuit of
knowledge demands a target, without which one cannot determine the object of inquiry or …

Image deblurring is an essential image restoration problem arising in several fields from
astronomy to medical science. It amounts to restoring an image from a degraded, blurry and …