Compressed sensing (CS) has been extensively used with wide spread application in MRI and other signal processing fields. Spark of the sensing matrix is at the heart of the CS framework for determining the success of the signal recovery for a given designed CS system. However, estimation of Spark of the sensing matrix is a combinatorial process, thus, practically difficult to estimate for realistic sizes of sensing matrices. The purpose of this work is to present a new optimization-problem-based approach for estimation of the Spark of the sensing matrix which will overcome the existing limitations, thereby, a tool to assess and design CS framework based systems.
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