Sparsity of representations of signals has been shown to be a key concept of fundamental
importance in fields such as blind source separation, compression, sampling and signal …

This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …

We present a novel formulation to removing reflection from polarized images in the wild. We
first identify the misalignment issues of existing reflection removal datasets where the …

In the era of data deluge,“big data” generated by mechanical equipment creates higher
requirements for the field of mechanical fault diagnosis. Intelligent diagnosis methods have …

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 …

We propose a physically-based approach to separate reflection using multiple polarized
images with a background scene captured behind glass. The input consists of three …

This study reviews the recent advances in data-driven polarimetric imaging technologies
based on a wide range of practical applications. The widespread international research and …

We address the problem of blind separation of multiple source layers from their linear
mixtures with unknown mixing coefficients and unknown layer motions. Such mixtures can …

When we take photos through glass windows or doors, the transmitted background scene is
often blended with undesirable reflection. Separating two layers apart to enhance the image …

In recent years, deep network pruning has attracted significant attention in order to enable
the rapid deployment of AI into small devices with computation and memory constraints …