65 citations · 135 across the 14 of their papers we have counts for
14 papers
Deep Scene-Driven Ordering of Hadamard Basis for Single-Pixel Spectral Imaging
Brayan Monroy, Hans Garcia, Henry Arguello +1
Spectral images are highly valuable for various applications, including environmental monitoring and precision agriculture. However, the high cost of specialized sensors limits the…
Projection-Based Correction for Enhancing Deep Inverse Networks
Jorge Bacca
Deep learning-based models have demonstrated remarkable success in solving illposed inverse problems; however, many fail to strictly adhere to the physical constraints imposed by t…
Hadamard Row-Wise Generation Algorithm
Brayan Monroy, Jorge Bacca
In this paper, we introduce an efficient algorithm for generating specific Hadamard rows, addressing the memory demands of pre-computing the entire matrix. Leveraging Sylvester's r…
Designed Dithering Sign Activation for Binary Neural Networks
Brayan Monroy, Juan Estupiñan, Tatiana Gelvez-Barrera +2
Binary Neural Networks emerged as a cost-effective and energy-efficient solution for computer vision tasks by binarizing either network weights or activations. However, common bina…
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…
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…
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…
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…
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…
Compressive Spectral Image Reconstruction using Deep Prior and Low-Rank Tensor Representation
Jorge Bacca, Yesid Fonseca, Henry Arguello
Compressive spectral imaging (CSI) has emerged as an alternative spectral image acquisition technology, which reduces the number of measurements at the cost of requiring a recovery…
Exact Crystalline Structure Recovery in X-ray Crystallography from Coded Diffraction Patterns
Samuel Pinilla, Jorge Bacca, Cesar Vargas +2
X-ray crystallography (XC) is an experimental technique used to determine three-dimensional crystalline structures. The acquired data in XC, called diffraction patterns, is the Fou…
SPRSF: Sparse Phase Retrieval via Smoothing Function
Samuel Pinilla, Jorge Bacca, Henry Arguello
Phase retrieval (PR) is an ill-conditioned inverse problem which can be found in various science and engineering applications. Assuming sparse priority over the signal of interest,…