Introducing nonuniform sparse proximal averaging network for seismic reflectivity inversion
We consider the problem of seismic reflectivity inversion, which pertains to the high-
resolution recovery of interface locations and reflection coefficients from seismic …
resolution recovery of interface locations and reflection coefficients from seismic …
Quantized proximal averaging networks for compressed image recovery
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 …
convex sparsity promoting penalties. The multi-penalty formulation results in an iterative …
Tight-Frame-Like Analysis-Sparse Recovery Using Nontight Sensing Matrices
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 …
sensing matrices satisfy the restricted isometry property, which is crucial for solving the …
Tight-frame-like Sparse Recovery Using Non-tight Sensing Matrices
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 …
matrices possess the desirable restricted isometry property (RIP), which is crucial for …
An ensemble of proximal networks for sparse coding
Sparse coding methods are iterative and typically rely on proximal gradient methods. While
the commonly used sparsity promoting penalty is the ℓ 1 norm, alternatives such as the …
the commonly used sparsity promoting penalty is the ℓ 1 norm, alternatives such as the …
Full Waveform Inversion with Low-Frequency Extrapolation
Full waveform inversion (FWI) is an advanced seismic processing technology for
reconstructing high-resolution, subsurface geophysical models utilizing entire waveform …
reconstructing high-resolution, subsurface geophysical models utilizing entire waveform …