UTOPY trains unrolling algorithms for ill-posed inverse problems via a fidelity homotopy path from synthetic well-posed to real ill-posed sensing operators, yielding up to 2.5 dB PSNR gains.
Hompinns: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions,
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UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
UTOPY trains unrolling algorithms for ill-posed inverse problems via a fidelity homotopy path from synthetic well-posed to real ill-posed sensing operators, yielding up to 2.5 dB PSNR gains.