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Compressive Hyperspectral Imaging Using Progressive Total Variation
Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral images for earth observation, since it could exploit the strong spatial and spectral correlations, llowing to simplify the architecture of the onboard sensors.
Smoothness-Constrained Image Recovery from Block-Based Random Projections
On one hand, it allows to enforce a set of constraints to drive the reconstruction algorithm towards a smooth solution, imposing the similarity of block borders.
A Novel Progressive Image Scanning and Reconstruction Scheme based on Compressed Sensing and Linear Prediction
The use of CS in such setting raises the problem of reconstructing a very high number of samples, as are contained in an image, from their linear projections.
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