1 citations · 2 across the 9 of their papers we have counts for
10 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…
Learning to reconstruct signals with inexact sensing operator via knowledge distillation
Roman Jacome, Leon Suarez, Romario Gualdrón-Hurtado +2
In computational optical imaging and wireless communications, signals are acquired through linear coded and noisy projections, which are recovered through computational algorithms.…
Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach
Leon Suarez-Rodriguez, Roman Jacome, Henry Arguello
Computational optical imaging (COI) systems have enabled the acquisition of high-dimensional signals through optical coding elements (OCEs). OCEs encode the high-dimensional signal…
Learning Point Spread Function Invertibility Assessment for Image Deconvolution
Romario Gualdrón-Hurtado, Roman Jacome, Sergio Urrea +2
Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches th…
An Invitation to Hypercomplex Phase Retrieval: Theory and Applications
Roman Jacome, Kumar Vijay Mishra, Brian M. Sadler +1
Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing intrinsic correlation of the signal dimensions through Cliffor…