9 papers
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…
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…
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…
Feature Fusion via Dual-resolution Compressive Measurement Matrix Analysis For Spectral Image Classification
Juan Marcos Ramirez, Jose Ignacio Martinez-Torre, Henry Arguello
In the compressive spectral imaging (CSI) framework, different architectures have been proposed to recover high-resolution spectral images from compressive measurements. Since CSI…