11 accepted papers
This study focuses on multiple target detection in the presence of signal-dependent clutter using a Multiple-Input Multiple-Output (MIMO) radar system. The problem is formulated as worst-case SINR maximization (max-min optimization), which is a function of the MIMO waveform, under the hardware-inspi…
Joint low-light enhancement and deblurring is a challenging imaging inverse problem that estimates clean images from photography corrupted by both low-light and blurring artifacts. To address this task, we propose FELI, a Fast and physically Enriched deep neural network for joint Low-light enhanceme…
Optimizing a transmit MIMO radar waveform subject to the non-convex constant modulus constraint remains a problem of enduring interest. The past decade has seen a variety of tailored iterative approaches with various performance-complexity trade-offs. Despite promising work, iterative algorithms hav…
Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet significant challenges remain in: 1) predicting a large…
We address the problem of 3-D blood vessel segmentation with a deep learning method that incorporates domain information via priors and regularizers on vessel structure and morphology. Inspired by the observation that 3-D vessel structures project onto 2-D image slices with distinctive edges that ca…
High Dynamic Range (HDR) reconstruction is the process of producing an HDR image from a set of Standard Dynamic Range (SDR) images with different exposure times. This is a particularly challenging problem when relative camera or object motion exists between the available SDR images. Recently, deep l…
Image dehazing continues to be one of the most challenging inverse problems. Deep learning methods have emerged to complement traditional model-based methods and have helped define a new state of the art in achievable dehazed image quality. However, most deep learning-based methods usually design a…
While neural networks have achieved vastly enhanced performance over traditional iterative methods in many cases, they are generally empirically designed and the underlying structures are difficult to interpret. The algorithm unrolling approach has helped connect iterative algorithms to neural netwo…
Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR) images/patches with the help of training examples…
Deep learning methods, in particular trained Convolutional Neural Networks (CNNs) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the low resolution (LR) image to its corresponding high resolution (HR…
Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification. It is well-known that solving for the sparse coef…