9 papers
DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems
Romario Gualdrón-Hurtado, Roman Jacome, Leon Suarez +1
Solving imaging inverse problems has usually been addressed by designing proper prior models of the underlying signal. However, minimizing the data fidelity term poses significant…
GSNR: Graph Smooth Null-Space Representation for Inverse Problems
Romario Gualdrón-Hurtado, Roman Jacome, Rafael S. Suarez +1
Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the measurements due to the non-trivial null-space of the sensing matrix. Common ima…
NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems
Roman Jacome, Romario Gualdrón-Hurtado, Leon Suarez +1
Imaging inverse problems aim to recover high-dimensional signals from undersampled, noisy measurements, a fundamentally ill-posed task with infinite solutions in the null-space of…
DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstruction
Leon Suarez-Rodriguez, Roman Jacome, Romario Gualdron-Hurtado +2
Sparse-view computed tomography (CT) reconstruction is fundamentally challenging due to undersampling, leading to an ill-posed inverse problem. Traditional iterative methods incorp…
UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
Roman Jacome, Romario Gualdrón-Hurtado, Leon Suarez-Rodriguez +1
Imaging Inverse problems aim to reconstruct an underlying image from undersampled, coded, and noisy observations. Within the wide range of reconstruction frameworks, the unrolling…
Deep Distillation Gradient Preconditioning for Inverse Problems
Romario Gualdrón-Hurtado, Roman Jacome, Leon Suarez +2
Imaging inverse problems are commonly addressed by minimizing measurement consistency and signal prior terms. While huge attention has been paid to developing high-performance prio…