Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

We give a methodology-oriented perspective on directional image analysis and rotation-
invariant processing. We review the state of the art in the field and make connections with …

Deep convolutional neural networks (DCNNs) have led to breakthrough results in numerous
practical machine learning tasks, such as classification of images in the ImageNet data set …

Many trait-specific countermeasures to face spoofing attacks have been developed for
security of face authentication. However, there is no superior face anti-spoofing technique to …

The high complexity of various inverse problems poses a significant challenge to model-
based reconstruction schemes, which in such situations often reach their limits. At the same …

In this article we develop an image based rendering technique based on light field
reconstruction from a limited set of perspective views acquired by cameras. Our approach …

Wavelets and their associated transforms are highly efficient when approximating and
analyzing one-dimensional signals. However, multivariate signals such as images or videos …

It is a challenging task to reconstruct images from their noisy, blurry, and/or incomplete
measurements, especially those with important details and features such as medical …

Parsimonious representations are ubiquitous in modeling and processing information.
Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we …

This thoroughly updated new edition presents state of the art sparse and multiscale image
and signal processing. It covers linear multiscale geometric transforms, such as wavelet …