Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

For seismic random noise attenuation, deep learning has attracted much attention and
achieved promising performance. However, compared with conventional methods, the …

Dictionary learning was introduced for sparse image representation. Today, it is a
cornerstone of image classification. We propose a novel dictionary learning method to …

Convolutional neural networks (CNNs) have been tremendously successful in solving
imaging inverse problems. To understand their success, an effective strategy is to construct …

We propose-Deep Simplex (KDS) which, given a set of data points, learns a dictionary
comprising synthetic landmarks, along with representation coefficients supported on a …

The dictionary learning problem, representing data as a combination of a few atoms, has
long stood as a popular method for learning representations in statistics and signal …

The problem of sparse multichannel blind deconvolution (S-MBD) arises frequently in many
engineering applications such as radar/sonar/ultrasound imaging. To reduce its …

Latent variable models (LVMs) represent observed variables by parameterized functions of
latent variables. Prominent examples of LVMs for unsupervised learning are probabilistic …

Latent Gaussian models have a rich history in statistics and machine learning, with
applications ranging from factor analysis to compressed sensing to time series analysis. The …