24 accepted papers
Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the measurements due to the non-trivial null-space of the sensing matrix. Common image priors promote solutions on the general image manifold, such as sparsity, smoothness, or score function. However, as
Ensuring the authenticity and ownership of digital images is increasingly challenging as modern editing tools enable highly realistic forgeries. Existing image protection systems mainly rely on digital watermarking, which is susceptible to sophisticated digital attacks. To address this limitation, w
Compressive imaging (CI) consists of reconstructing images from incomplete observed data. The reconstruction process involves solving an ill-posed inverse problem which is highly dependent on the number of real measurements, with a greater number of measurements typically leading to more accurate re…
In compressive imaging systems, the scene is acquired via linear coded noisy projections, known as measurements, requiring a recovery process to estimate the underlying signal. This recovery is inherently ill-posed, posing a challenge for accurate signal recovery. Existing methods that employ prior…
In computational optical imaging and wireless communications, signals are acquired through linear coded and noisy projections, which are recovered through computational algorithms. Deep model-based approaches, i.e., neural networks incorporating the sensing operators, are the state-of-the-art for si…
Imaging inverse problems aim to recover high-dimensional signals from undersampled, noisy measurements, a fundamentally ill-posed task with infinite solutions in the null-space of the sensing operator. To resolve this ambiguity, prior information is typically incorporated through handcrafted regular…
In remote sensing and environmental monitoring, the authenticity of the collected hyperspectral image (HSI) is critical since unauthorized changes could cause inaccurate evaluation. Traditional approaches ensure HSI integrity after the acquisition, leaving the data vulnerable to unauthorized access…
Quantized neural networks employ reduced precision representations for both weights and activations. This quantization process significantly reduces the memory requirements and computational complexity of the network. Binary Neural Networks (BNNs) are the extreme quantization case representing value…
Commercial sensors often suffer from overexposure in bright regions, leading to signal clipping and information loss because of saturation. Existing solutions involve either employing logarithmic irradiance response sensors or capturing multiple shots from different saturation levels. However, these…
Recent developments in spectrum-sharing technologies include integrated sensing and communications (ISAC) systems to save resources, cost, and power. In this paper, we consider a co-existence topology with n-tuple radar and communications transmitters, wherein neither the transmitted signal nor the…
This paper introduces a plug-and-play algorithm for enhancing compressive spectral imaging (CSI) through the integration of both a quadratic envelope (QE) regularizer and a deep prior. Our method employs the QE-based regularizer to foster a low-rank structure in conjunction with deep priors, synergi…
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, preserving privacy in deep learning systems has become a critical concern. Existing methods for privacy preservation rely…
The modern surge in camera usage alongside widespread computer vision technology applications poses significant privacy and security concerns. Current artificial intelligence (AI) technologies aid in recognizing relevant events and assisting in daily tasks in homes offices hospitals etc. The need to…
Hadamard single-pixel imaging (HSI) is a promising sensing approach for acquiring spectral images in the near-infrared spectrum with high spatial resolution and fast recovery times due to the efficient invertible properties of the Hadamard matrix. The potential of the HSI system is diminished becaus…
We consider a general spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In this dual-blind deconvolution (DBD) problem, a common receiver admits the multi-carrier wireless communications signal that is overl…
"The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our fr…
This paper presents a uniqueness result which states that a band- limited signal can be recovered from at least 3B measurements where B is the bandwidth from the radar ambiguity function (AF). This function is a two-dimensional mapping of the propagation delay and Doppler frequency. This formal mode…
The widespread use of always-connected digital cameras in our everyday life has led to increasing concerns about the users' privacy and security. How to develop privacy-preserving computer vision systems? In particular, we want to prevent the camera from obtaining detailed visual data that may conta…
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. The performance of compressive imaging systems based on CAs mostly depends on two factors: the properties of the m…
Deep learning End-to-End (E2E) approaches have emerged as alternative optical design models, which jointly train the optical parameters of the sensing protocol, and the parameters of the deep neural network to achieve a specific task. This E2E model is particularly useful in the design of coding opt…
Coded aperture compressive spectral imagers allow sensing a three-dimensional (3D) data cube by using two-dimensional (2D) projections of the coded and spectrally dispersed source. The traditional block-unblock coded apertures have been recently replaced by patterned optical filter arrays, allowing…
Phase retrieval is a kind of ill-posed inverse problem, which is present in various applications, such as optics, astronomical imaging, and X-ray crystallography. Mathematically this inverse problem consists on recovering an unknown signal x ∈ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xm…
This paper introduces a hierarchical Bayesian model for the reconstruction of hyperspectral images using compressed sensing measurements. This model exploits known properties of natural images, promoting the recovered image to be sparse on a selected basis and smooth in the image domain. The posteri…
X-ray crystallography is an experimental technique to estimate the 3D atomic positions of the elements present in a crystal. This technique constructs the 3D structure from the phase of diffracted and patterned X-rays (DPX). Multiple intensity DPX measurements are acquired to solve the phase retriev…