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Found 7 papers, 2 papers with code
Ray-Space Motion Compensation for Lenslet Plenoptic Video Coding
Plenoptic images and videos bearing rich information demand a tremendous amount of data storage and high transmission cost.
Multi-Scale Deep Compressive Imaging
Recently, deep learning-based compressive imaging (DCI) has surpassed the conventional compressive imaging in reconstruction quality and faster running time.
Restricted Structural Random Matrix for Compressive Sensing
RSRM acquired compressive measurements with random projection (equally important) of multiple randomly sub-sampled signals, which was restricted to be the low-resolution signals (equal in energy), thereby, its observations are equally important.
Multi-Scale Deep Compressive Sensing Network
With joint learning of sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction.
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary
The multiple sub-dictionaries contained in the higher order dictionary decorrelate the group in each corresponding dimension, thus help the detail of color images to be reconstructed better.
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation
Although block compressive sensing (BCS) makes it tractable to sense large-sized images and video, its recovery performance has yet to be significantly improved because its recovered images or video usually suffer from blurred edges, loss of details, and high-frequency oscillatory artifacts, especially at a low subrate.
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Total variation has proved its effectiveness in solving inverse problems for compressive sensing.
