Compressive Sensing with Redundant Dictionaries and Structured Measurements
This work provides theoretical guarantees for compressive sensing with structured measurements and redundant dictionaries, addressing a gap between random matrix theory and practical sensing systems.
The authors prove that subsampling a fixed orthogonal matrix can satisfy the D-restricted isometry property (D-RIP) for redundant dictionaries, provided the matrix is sufficiently incoherent with the dictionary. This yields recovery guarantees for structured measurements in compressive sensing, enabling practical applications.
Consider the problem of recovering an unknown signal from undersampled measurements, given the knowledge that the signal has a sparse representation in a specified dictionary $D$. This problem is now understood to be well-posed and efficiently solvable under suitable assumptions on the measurements and dictionary, if the number of measurements scales roughly with the sparsity level. One sufficient condition for such is the $D$-restricted isometry property ($D$-RIP), which asks that the sampling matrix approximately preserve the norm of all signals which are sufficiently sparse in $D$. While many classes of random matrices are known to satisfy such conditions, such matrices are not representative of the structural constraints imposed by practical sensing systems. We close this gap in the theory by demonstrating that one can subsample a fixed orthogonal matrix in such a way that the $D$-RIP will hold, provided this basis is sufficiently incoherent with the sparsifying dictionary $D$. We also extend this analysis to allow for weighted sparse expansions. Consequently, we arrive at compressive sensing recovery guarantees for structured measurements and redundant dictionaries, opening the door to a wide array of practical applications.