MRI is one of the most dynamic and safe imaging techniques available in the clinic today.
However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential …

With the increasing use of multimedia technologies, image compression requires higher
performance as well as new features. To address this need in the specific area of still image …

We propose a novel neural representation for videos (NeRV) which encodes videos in
neural networks. Unlike conventional representations that treat videos as frame sequences …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

We present a machine learning-based approach to lossy image compression which
outperforms all existing codecs, while running in real-time. Our algorithm typically produces …

Deep neural networks, in particular convolutional neural networks, have become highly
effective tools for compressing images and solving inverse problems including denoising …

CNNs are poised to become integral parts of many critical systems. Despite their robustness
to natural variations, image pixel values can be manipulated, via small, carefully crafted …

There have been several empirical studies addressing breast cancer using machine
learning and soft computing techniques. Many claim that their algorithms are faster, easier …

Inspired by the great success of deep neural networks (DNNs), many unfolding methods
have been proposed to integrate traditional image modeling techniques, such as dictionary …