Efficient Nonlinear Compressed Sensing for Electrical Impedance Tomography Reconstruction

The authors propose an Oracle-Net, a graph neural network, to predict the support of the sparse solution in a variational framework for nonlinear compressed sensing in Electrical Impedance Tomography reconstruction problems. The derived nonsmooth optimization problem is efficiently solved through a constrained proximal gradient method, providing error bounds on the approximate solution.