KEYWORDS: Associative arrays, Visualization, Visual process modeling, Systems modeling, Matrices, Compressed sensing, Statistical analysis, Signal processing, Signal generators, Performance modeling
In this paper, we consider the recovery of the high-dimensional block-sparse signal from a compressed set of measurements, where the nonzero coefficients of the recovered signal occur in several blocks. Adopting the idea of deep unfolding, we explore the block-sparse structure and put forward a block-sparse reconstruction network named Ada-BlockLISTA, which performs gradient descent on every single block followed by a block-wise shrinkage. Furthermore, we prove the linear convergence rate of our proposed network, which also theoretically guarantees exact recovery for a potentially higher sparsity level based on the underlying block structure. Numerical results indicate that Ada-BlockLISTA yields better signal recovery performance than existing algorithms, which ignore the additional block structure in the signal model.
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