16 accepted papers
Recent advances in solving imaging inverse problems have witnessed the combination of deep learning models with classical image models for better signal representation. One such approach, DeepRED, combines the deep image prior (DIP) with the regularization by denoising (RED) framework to boost the p…
In several applications, including imaging of deformable objects while in motion, simultaneous localization and mapping, and unlabeled sensing, we encounter the problem of recovering a signal that is measured subject to unknown permutations. In this paper we take a fresh look at this problem through…
We address the problem of sharpening low spatial-resolution multi-spectral (MS) images with their associated misaligned high spatial-resolution panchromatic (PAN) image, based on priors on the spatial blur kernel and on the cross-channel relationship. In particular, we formulate the blind pan-sharpe…
We study the problem of reconstructing an object from phaseless measurements in the context of inverse multiple scattering. Our formulation explicitly decouples the variables that represent the unknown object image and the unknown phase, respectively, in the forward model. This enables us to simulta…
Plug-and-play (PnP) denoising for solving inverse problems has received significant attention recently thanks to its state of the art signal reconstruction performance. However, the performance improvement hinges on carefully choosing the noise level of the Gaus-sian denoiser and the descent step si…
Parameter estimation of damped exponential signals has wide applications including fault detection and system parameter identification, etc. However, existing methods for estimating parameters of damped exponentials are either sensitive to noise or restricted to dealing with a certain type of noise…
In this paper we develop an optimization-based solution to the problem of distributed radar imaging using antennas with asynchronous clocks. In particular, we consider a distributed radar imaging MIMO system observing a sparse scene under an unknown, but bounded, delay between the transmitter and re…
Reflection tomography is an inverse scattering technique that estimates the spatial distribution of an object's permittivity by illuminating it with a probing pulse and measuring the scattered wavefields by receivers located on the same side as the transmitter. Unlike conventional transmission tomog…
In this paper, we consider the problem of fusing low spatial resolution multi-spectral (MS) aerial images with their associated high spatial resolution panchromatic image. To solve this problem, various methods have been proposed, using either model-based or model-agnostic algorithms such as deep le…
The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications. While the most popular formulations of the problem are based on linearizing the object-light relationship, there is an increased interest in considering nonlinear formul…
Multi-spectral (MS) image super-resolution aims to reconstruct super-resolved multi-channel images from their low-resolution images by regularizing the image to be reconstructed. Recently data-driven regularization techniques based on sparse modeling and deep learning have achieved substantial impro…
The radar autofocus problem arises in situations where radar measurements are acquired of a scene using antennas that suffer from position ambiguity. Current techniques model the antenna ambiguity as a global phase error affecting the received radar measurement at every antenna. However, the phase e…
We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements. Our method relies on a novel, nonlinear measurement model that can account for the multiple scattering phenomenon, which makes the method preferable in applications where lin…
We present a new method for joint automatic extrinsic calibration and sensor fusion for a multimodal sensor system comprising a LIDAR and an optical camera. Our approach exploits the natural alignment of depth and intensity edges when the calibration parameters are correct. Thus, in contrast to a nu…
We propose a new method for low-complexity compression of multispectral images. We develop on a novel approach to coding signals with side information based on recent advances in compressed sensing and universal scalar quantization. Our approach can be interpreted as a variation of quantized compres…
Continuous improvement in optical sensing components, as well as recent advances in signal acquisition theory provide a great opportunity to reduce the cost and enhance the capabilities of depth sensing systems. In this paper we propose a new depth sensing architecture that exploits a fixed coded ap…