8 papers
Frozen CLIP Priors for Robust Self-Supervised Poisson Inverse Problems
Laura C. Diaz-Delgado, Emmanuel Martinez, Henry Arguello
Self-supervised learning for imaging inverse problems is increasingly important in photon-limited settings, where acquiring clean ground truth is impractical and reconstruction mus…
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…
Distilling Knowledge for Designing Computational Imaging Systems
Leon Suarez-Rodriguez, Roman Jacome, Henry Arguello
Designing the physical encoder is crucial for accurate image reconstruction in computational imaging (CI) systems. Currently, these systems are designed via end-to-end (E2E) optimi…