Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …

For medical image analysis, there is always an immense need for rich details in an image.
Typically, the diagnosis will be served best if the fine details in the image are retained and …

Deep convolutional networks–based super-resolution is a fast-growing field with numerous
practical applications. In this exposition, we extensively compare more than 30 state-of-the …

Conclusion The standard approach of producing images with deconvolution—despite its
successes!—has some conceptually simple issues that lead to artifacts in produced images …

Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to
identifying images and/or image substructures that deviate significantly from the norm …

We introduce the adversarially learned inference (ALI) model, which jointly learns a
generation network and an inference network using an adversarial process. The generation …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

We present a new approach to learn compressible representations in deep architectures
with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation …

High dynamic range (HDR) imaging provides the capability of handling real world lighting as
opposed to the traditional low dynamic range (LDR) which struggles to accurately represent …