12 accepted papers
Motor current signature analysis (MCSA) has been widely used in motor fault diagnosis by extracting characteristic frequency components in the spectrum of the stator current. However, fault signatures in the motor current are generally weak and easily influenced by noise and spectrum distortion caus…
We address the problem of enabling two-dimensional digital image correlation (DIC) for strain measurement on large three-dimensional objects with curved surfaces. It is challenging to acquire full-field qualified images of the surface required by DIC due to geometric distortion and the narrow visual…
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…
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…
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…
Tensor robust principal component analysis (PCA) approaches have drawn considerable interests in many applications such as background subtraction, denoising, and outlier detection, etc. In this paper we propose an online tensor robust PCA where the multidimensional data (tensor) is revealed sequenti…