Snapshot compressive imaging: Theory, algorithms, and applications
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 (≥ …
related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥ …
Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling
The compressive sensing (CS) scheme exploits many fewer measurements than suggested
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
ADMM-CSNet: A deep learning approach for image compressive sensing
Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …
amount of sampled data. It has been widely applied in medical imaging, remote sensing …
Rank minimization for snapshot compressive imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple
frames are mapped into a single measurement, with video compressive imaging and …
frames are mapped into a single measurement, with video compressive imaging and …
Plug-and-play algorithms for large-scale snapshot compressive imaging
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 …
images using a 2D sensor (detector) in a single snapshot. Though enjoying the advantages …
Deep unfolding for snapshot compressive imaging
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 …
images in a single shot using 2D detectors. SCI devices consist of two main parts: a …
l-net: Reconstruct hyperspectral images from a snapshot measurement
We propose the l-net, which reconstructs hyperspectral images (eg, with 24 spectral
channels) from a single shot measurement. This task is usually termed snapshot …
channels) from a single shot measurement. This task is usually termed snapshot …
Recurrent neural networks for snapshot compressive imaging
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 …
consume a significant amount of memory and bandwidth to save and transmit the high …
Compressive sensing via nonlocal low-rank regularization
Sparsity has been widely exploited for exact reconstruction of a signal from a small number
of random measurements. Recent advances have suggested that structured or group …
of random measurements. Recent advances have suggested that structured or group …
Deep learning for video compressive sensing
We investigate deep learning for video compressive sensing within the scope of snapshot
compressive imaging (SCI). In video SCI, multiple high-speed frames are modulated by …
compressive imaging (SCI). In video SCI, multiple high-speed frames are modulated by …