1 citations · 1 across the 12 of their papers we have counts for
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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…
Scale Equivariance Regularization and Feature Lifting in High Dynamic Range Modulo Imaging
Brayan Monroy, Jorge Bacca
Modulo imaging enables high dynamic range (HDR) acquisition by cyclically wrapping saturated intensities, but accurate reconstruction remains challenging due to ambiguities between…
Deep Lightweight Unrolled Network for High Dynamic Range Modulo Imaging
Brayan Monroy, Jorge Bacca
Modulo-Imaging (MI) offers a promising alternative for expanding the dynamic range of images by resetting the signal intensity when it reaches the saturation level. Subsequently, h…
DeepInverse: A Python package for solving imaging inverse problems with deep learning
Julián Tachella, Matthieu Terris, Samuel Hurault +24
DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementa…
Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications
Brayan Monroy, Kebin Contreras, Jorge Bacca
High dynamic range (HDR) imaging is vital for capturing the full range of light tones in scenes, essential for computer vision tasks such as autonomous driving. Standard commercial…
Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise
Brayan Monroy, Jorge Bacca, Julián Tachella
Recorrupted-to-Recorrupted (R2R) has emerged as a methodology for training deep networks for image restoration in a self-supervised manner from noisy measurement data alone, demons…
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…