12 accepted papers
We introduce the Generative Barankin Bound (GBB), a learned Barankin Bound, for evaluating the achievable performance in estimating the direction of arrival (DOA) of a source in non-asymptotic conditions, when the statistics of the measurement are unknown. We first learn the measurement distribution…
Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this pap…
The Misspecified Cramér-Rao lower bound (MCRB) provides a lower bound on the performance of any unbiased estimator of parameter vector θ under model misspecification. An approximation of the MCRB can be numerically evaluated using a set of i.i.d samples of the true distribution at θ. However, obtain…
Dynamic imaging involves the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed. In this work, we…
We present an algorithm for the resolution of delayed and overlapping pulses of a common unknown shape from multi-channel measurements. We show that just a few Fourier samples acquired by a Time Encoding Machine (TEM) suffice to solve this challenging problem. This acquisition scheme is desired for…
Deep-learning-based methods for various applications have been shown vulnerable to adversarial examples. Here we address the use of deep-learning networks as inverse problem solvers, which has generated much excitement and even adoption efforts by the main equipment vendors for medical imaging inclu…
A Generative Adversarial Network (GAN) with generator G trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. Here, we propose a new method of deploying a GAN-based prior to solve linear inverse problems using projected…
Multichannel blind deconvolution is the problem of recovering an unknown signal f and multiple unknown channels x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> from convolutional measurements y <sub xmlns:mml="http://www.w3.org/1998/Math/MathM…
Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from convolutional measurements $y_i=x_i \circledast f$ ($i=1,2,\dots,N$). We consider the case where the $x_i$'s are sparse, and convolution with $f$ is invertible. Our nonconvex…
Data-driven and learning-based sparse signal models outperform analytical models (e.g, wavelets), for image denoising, but require careful parameter tuning to reach peak performance. In this work, we provide a solution to the problem of parameter tuning for image denoising with transform sparsity re…
Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach a…
Recent works on adaptive sparse signal modeling have demonstrated their usefulness in various image/video processing applications. As the popular synthesis dictionary learning methods involve NP-hard sparse coding and expensive learning steps, transform learning has recently received more interest f…