1 code implementation • 3 May 2024 • Brayan Monroy, Juan Estupiñan, Tatiana Gelvez-Barrera, Jorge Bacca, Henry Arguello
Binary Neural Networks emerged as a cost-effective and energy-efficient solution for computer vision tasks by binarizing either network weights or activations.
no code implementations • 5 Nov 2022 • 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 spectral imaging reconstruction.
no code implementations • 27 Jun 2022 • Henry Arguello, Jorge Bacca, Hasindu Kariyawasam, Edwin Vargas, Miguel Marquez, Ramith Hettiarachchi, Hans Garcia, Kithmini Herath, Udith Haputhanthri, Balpreet Singh Ahluwalia, Peter So, Dushan N. Wadduwage, Chamira U. S. Edussooriya
The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task.
no code implementations • 27 May 2022 • 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 costly such as in the near-infrared spectrum.
no code implementations • 24 May 2022 • Roman Jacome, Jorge Bacca, Henry Arguello
To overcome this issue, compressive spectral image fusion (CSIF) employs the projected measurements of two CSI architectures with different resolutions to estimate a high-spatial high-spectral resolution.
1 code implementation • 16 May 2022 • Brayan Monroy, Jorge Bacca, Henry Arguello
Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery.
1 code implementation • IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) 2021 • Brayan Monroy, Jorge Bacca, Henry Arguello
This paper proposes an autoencoder-based network that guarantees a low-dimensional spectral representation through feature reduction, which can be used as prior in the compressive spectral imaging reconstruction.
1 code implementation • 19 Jan 2021 • Jorge Bacca, Yesid Fonseca, Henry Arguello
The proposed method is based on the fact that the structure of some deep neural networks and an appropriated low-dimensional structure are sufficient to impose a structure of the underlying spectral image from CSI.