16 citations · 20 across the 18 of their papers we have counts for
19 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…
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…
Factor Graph Processing for Dual-Blind Deconvolution at ISAC Receiver
Roman Jacome, Edwin Vargas, Kumar Vijay Mishra +2
Integrated sensing and communications (ISAC) systems have gained significant interest because of their ability to jointly and efficiently access, utilize, and manage the scarce ele…
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…
Octonion Phase Retrieval
Roman Jacome, Kumar Vijay Mishra, Brian M. Sadler +1
Signal processing over hypercomplex numbers arises in many optical imaging applications. In particular, spectral image or color stereo data are often processed using octonion algeb…
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance Regularization
Emmanuel Martinez, Roman Jacome, Alejandra Hernandez-Rojas +1
Deep learning methods are state-of-the-art for spectral image (SI) computational tasks. However, these methods are constrained in their performance since available datasets are lim…
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
Roman Jacome, Edwin Vargas, Kumar Vijay Mishra +2
In joint radar-communications (JRC) applications such as secure military receivers, often the radar and communications signals are overlaid in the received signal. In these passive…
Dual-Blind Deconvolution for Overlaid Radar-Communications Systems
Edwin Vargas, Kumar Vijay Mishra, Roman Jacome +2
The increasingly crowded spectrum has spurred the design of joint radar-communications systems that share hardware resources and efficiently use the radio frequency spectrum. We st…
Multi-dimensional dual-blind deconvolution approach toward joint radar-communications
Roman Jacome, Kumar Vijay Mishra, Edwin Vargas +2
We consider a joint multiple-antenna radar-communications system in a co-existence scenario. Contrary to conventional applications, wherein at least the radar waveform and communic…
DUF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion
Roman Jacome, Jorge Bacca, Henry Arguello
Compressive spectral imaging (CSI) has attracted significant attention since it employs synthetic apertures to codify spatial and spectral information, sensing only 2D projections…
Joint Radar-Communications Processing from a Dual-Blind Deconvolution Perspective
Edwin Vargas, Kumar Vijay Mishra, Roman Jacome +2
We consider a general spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In this \textit…