Capturing high-dimensional (HD) data is a long-term challenge in signal processing and
related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥ …

Point set registration (PSR) is the process of computing a spatial transformation that
optimally aligns pairs of point sets. The method helps to amalgamate multiple datasets into a …

The goal of compressed sensing is to estimate a vector from an underdetermined system of
noisy linear measurements, by making use of prior knowledge on the structure of vectors in …

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple
frames are mapped into a single measurement, with video compressive imaging and …

Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D)
images using a 2D sensor (detector) in a single snapshot. Though enjoying the advantages …

Snapshot compressive imaging (SCI) systems aim to capture high-dimensional (≥ 3 D)
images in a single shot using 2D detectors. SCI devices consist of two main parts: a …

Conventional high-speed and spectral imaging systems are expensive and they usually
consume a significant amount of memory and bandwidth to save and transmit the high …

Snapshot compressive imaging (SCI) systems have been developed to capture high-
dimensional (> 3) signals using low-dimensional off-the-shelf sensors, ie, mapping multiple …

We consider the total variation (TV) minimization problem used for compressive sensing and
solve it using the generalized alternating projection (GAP) algorithm. Extensive results …

Hyperspectral imaging is an essential imaging modality for a wide range of applications,
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …