Optimized Sparse Projections for Compressive Sensing

We consider designing a sparse sensing matrix which contains few non-zero entries per row for compressive sensing (CS) systems. By unifying the previous approaches for optimizing sensing matrices based on minimizing the mutual coherence, we propose a general framework for designing a sparse sensing matrix that minimizes the mutual coherence of the equivalent dictionary and is robust to sparse representation error. An alternating minimization-based algorithm is proposed for designing sparse sensing matrices. Experiments with real images show that the obtained sparse sensing matrix (even each row is extremely sparse) significantly outperforms a random dense sensing matrix in terms of the signal recovery accuracy.