65 citations · 269 across the 30 of their papers we have counts for
30 papers
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…
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
Karen Sanchez, Carlos Hinojosa, Kevin Arias +4
Data augmentation is classically used to improve the overall performance of deep learning models. It is, however, challenging in the case of medical applications, and in particular…
AgroTIC: Bridging the gap between farmers, agronomists, and merchants through smartphones and machine learning
Carlos Hinojosa, Karen Sanchez, Ariolfo Camacho +1
In recent years, fast technological advancements have led to the development of high-quality software and hardware, revolutionizing various industries such as the economy, health,…
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…
Computational Spectral Imaging: A Contemporary Overview
Jorge Bacca, Emmanuel Martinez, Henry Arguello
Spectral imaging collects and processes information along spatial and spectral coordinates quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spect…
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…
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
Tatiana Gelvez-Barrera, Jorge Bacca, Henry Arguello
This paper proposes a non-data-driven deep neural network for spectral image recovery problems such as denoising, single hyperspectral image super-resolution, and compressive spect…
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach
Paul Goyes, Edwin Vargas, Claudia Correa +4
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Pre-processing approaches, such as missing trace or shot interpol…
Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications
Jonathan Monsalve, Edwin Vargas, Kumar Vijay Mishra +2
Recent interest in integrated sensing and communications has led to the design of novel signal processing techniques to recover information from an overlaid radar-communications si…
Fast Disparity Estimation from a Single Compressed Light Field Measurement
Emmanuel Martinez, Edwin Vargas, Henry Arguello
The abundant spatial and angular information from light fields has allowed the development of multiple disparity estimation approaches. However, the acquisition of light fields req…
Deep Optical Coding Design in Computational Imaging
Henry Arguello, Jorge Bacca, Hasindu Kariyawasam +10
Computational optical imaging (COI) systems leverage optical coding elements (CE) in their setups to encode a high-dimensional scene in a single or multiple snapshots and decode it…
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…
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens
Carlos Hinojosa, Miguel Marquez, Henry Arguello +3
The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose…
Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification
Jorge Bacca, Alejandra Hernandez-Rojas, Henry Arguello
Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing technique, primarily to sense spectral regions where traditional systems result in highly co…
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…
JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging
Brayan Monroy, Jorge Bacca, Henry Arguello
Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery. These methods use a deep neural network (DNN) as an image generator to learn non-linear ma…
Phase Retrieval for Radar Waveform Design
Samuel Pinilla, Kumar Vijay Mishra, Brian M. Sadler +1
The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform. Mathematically, it is…
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…
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification
Carlos Hinojosa, Esteban Vera, Henry Arguello
Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts…
Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks
Jorge Bacca, Tatiana Gelvez, Henry Arguello
Covering from photography to depth and spectral estimation, diverse computational imaging (CI) applications benefit from the versatile modulation of coded apertures (CAs). The ligh…
Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems
Edwin Vargas, Julien N. P. Martel, Gordon Wetzstein +1
Compressive imaging using coded apertures (CA) is a powerful technique that can be used to recover depth, light fields, hyperspectral images and other quantities from a single snap…
LADMM-Net: An Unrolled Deep Network For Spectral Image Fusion From Compressive Data
Juan Marcos Ramírez, José Ignacio Martínez Torre, Henry Arguello Fuentes
Image fusion aims at estimating a high-resolution spectral image from a low-spatial-resolution hyperspectral image and a low-spectral-resolution multispectral image. In this regard…
Subspace-Based Feature Fusion From Hyperspectral And Multispectral Image For Land Cover Classification
Juan Ramírez, Héctor Vargas, José Ignacio Martínez +1
In remote sensing, hyperspectral (HS) and multispectral (MS) image fusion have emerged as a synthesis tool to improve the data set resolution. However, conventional image fusion me…