3 citations · 3 across the 1 of their papers we have counts for
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Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach
Leon Suarez-Rodriguez, Roman Jacome, Henry Arguello
Computational optical imaging (COI) systems have enabled the acquisition of high-dimensional signals through optical coding elements (OCEs). OCEs encode the high-dimensional signal…
Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning
Fabian Perez, Jhon Lopez, Henry Arguello
In the era of cloud computing and data-driven applications, it is crucial to protect sensitive information to maintain data privacy, ensuring truly reliable systems. As a result, p…
BiPer: Binary Neural Networks using a Periodic Function
Edwin Vargas, Claudia Correa, Carlos Hinojosa +1
Quantized neural networks employ reduced precision representations for both weights and activations. This quantization process significantly reduces the memory requirements and com…
Privacy-preserving Optics for Enhancing Protection in Face De-identification
Jhon Lopez, Carlos Hinojosa, Henry Arguello +1
The modern surge in camera usage alongside widespread computer vision technology applications poses significant privacy and security concerns. Current artificial intelligence (AI)…
Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperture
Jhon Lopez, Edwin Vargas, Henry Arguello
Depth estimation from a single image of a conventional camera is a challenging task since depth cues are lost during the acquisition process. State-of-the-art approaches improve th…
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…