Artificial intelligence tasks across numerous applications require accelerators for fast and
low-power execution. Optical computing systems may be able to meet these domain-specific …

This is a comprehensive, critical, and pedagogical review of volumetric emission
tomography for combustion processes. Many flames that are of interest to scientists and …

Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Deep learning has been proven to yield reliably generalizable solutions to numerous
classification and decision tasks. Here, we demonstrate for the first time to our knowledge …

In the study of compressed sensing (CS), the two main challenges are the design of
sampling matrix and the development of reconstruction method. On the one hand, the …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

We design a novel network architecture for learning discriminative image models that are
employed to efficiently tackle the problem of grayscale and color image denoising. Based on …

We propose a novel deep network architecture for grayscale and color image denoising that
is based on a non-local image model. Our motivation for the overall design of the proposed …

Sparse representation models code an image patch as a linear combination of a few atoms
chosen out from an over-complete dictionary, and they have shown promising results in …

Convolutional neural networks (CNNs) have gained tremendous success in solving complex
inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct …