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Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
Model-based deep learning methods such as \emph{loop unrolling} (LU) and \emph{deep equilibrium model} (DEQ) extensions offer outstanding performance in solving inverse problems (IP).
Learned Proximal Operator for Solving Seismic Deconvolution Problem
Due to the sparse nature of the reflectivity sequence, spike-promoting regularizers such as the $\ell_1$-norm are frequently used.
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver
In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements.
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