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The standard compressive sensing (CS) theory can be improved for robust recovery with fewer measurements under the assumption that signals lie on a union of subspaces (UoS). However, the UoS model is restricted to specific types of signal regularities with predetermined topology for subspaces. This paper proposes a generalized model which adaptively decomposes signals into a union of data-driven subspaces...
This paper proposes an adaptive dictionary learning approach based on sub modular optimization. A candidate atom set is constructed based on multiple bases from the combination of analytic and trained dictionaries. With the low-frequency components by the analytic DCT atoms, high-resolution dictionaries can be inferred through online learning to make efficient approximation with rapid convergence...
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