Spark Deficient Gabor Frame Provides A Novel Analysis Operator For Compressed Sensing

The analysis sparsity model is a very effective approach in modern Compressed Sensing applications. Specifically, redundant operators can lead to fewer measurements needed for reconstruction when employing the \(l_1\)-minimization Sensing. In this paper, we pick an eigenvector of Zauner unitary matrix and –under certain assumptions on ambient dimension– build spark deficient Gabor frame. operator associated with such frame, new (highly) transform, which use as sparsifier We conduct computational experiments –on both synthetic real-world data– solving problem Sensing, four different choices operators, including our operator. results show that proposed transform outperforms –in all cases– transforms generated by state-of-the-art window vectors time-frequency analysis.