Search Results for author:
Found 27 papers, 8 papers with code
Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning
To address these challenges, we propose a novel Privacy-Preserving framework that uses a set of deformable operators for secure task learning.
BiPer: Binary Neural Networks using a Periodic Function
In contrast to current BNN approaches, we propose to employ a binary periodic (BiPer) function during binarization.
Privacy-preserving Optics for Enhancing Protection in Face De-identification
Specifically, our approach first learns an optical encoder along with a regression model to obtain a face heatmap while hiding the face identity from the source image.
An Invitation to Hypercomplex Phase Retrieval: Theory and Applications
The hypercomplex PR (HPR) arises in many optical imaging and computational sensing applications that usually comprise quaternion and octonion-valued signals.
Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperture
Depth estimation from a single image of a conventional camera is a challenging task since depth cues are lost during the acquisition process.
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
This paper introduces a new data augmentation architecture that generates synthetic multiparametric (T1 arterial, T1 portal, and T2) magnetic resonance images (MRI) of massive macrotrabecular subtype hepatocellular carcinoma with their corresponding tumor masks through a generative deep learning approach.
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance Regularization
To surmount this limitation, we propose low-dimensional GAN (LD-GAN), where we train the GAN employing a low-dimensional representation of the {dataset} with the latent space of a pretrained autoencoder network.
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
In these passive listening outposts, the signals and channels of both radar and communications are unknown to the receiver.
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging.
Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications
In this dual-blind deconvolution (DBD) problem, the receiver admits a multi-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets.
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
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.
Fast Disparity Estimation from a Single Compressed Light Field Measurement
Specifically, we propose to jointly optimize an optical architecture for acquiring a single coded light field snapshot and a convolutional neural network (CNN) for estimating the disparity maps.
Deep Optical Coding Design in Computational Imaging
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.
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens
The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition.
Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification
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.
D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion
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.
JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging
Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery.
Phase Retrieval for Radar Waveform Design
The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform.
Deep Low-Dimensional Spectral Image Representation for Compressive Spectral Reconstruction
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.
Joint Radar-Communications Processing from a Dual-Blind Deconvolution Perspective
When the radar receiver is not collocated with the transmitter, such as in passive or multistatic radars, the transmitted signal is also unknown apart from the target parameters.
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification
Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity.
Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems
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 snapshot.
Subspace-Based Feature Fusion From Hyperspectral And Multispectral Image For Land Cover Classification
In remote sensing, hyperspectral (HS) and multispectral (MS) image fusion have emerged as a synthesis tool to improve the data set resolution.
Compressive Spectral Image Reconstruction using Deep Prior and Low-Rank Tensor Representation
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.
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based Algorithm
The algorithm estimates the covariance matrix of hyperspectral images from synthetic and real compressive samples.
Learning Privacy-Preserving Optics for Human Pose Estimation
However, we also want the camera to capture useful information to perform computer vision tasks.
Feature Fusion via Multiresolution Compressive Measurement Matrix Analysis For Spectral Image Classification
In this paper, a method that fuses features directly from multiresolution compressive measurements is proposed for spectral image classification.
