Compressed Sensing for Block-Sparse Smooth Signals
This work addresses signal reconstruction for compressed sensing applications, but it appears incremental as it builds on existing LASSO and regularization techniques without introducing a fundamentally new paradigm.
The paper tackles the problem of reconstructing smooth signals with block sparsity from compressed measurements by addressing varying group sizes using group-sparse LASSO and latent group LASSO regularizations, achieving smoothness through fusion, and developing low-complexity solvers via the alternating direction method of multipliers.
We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements. We tackle the issue of varying group size via group-sparse least absolute shrinkage selection operator (LASSO) as well as via latent group LASSO regularizations. We achieve smoothness in the signal via fusion. We develop low-complexity solvers for our proposed formulations through the alternating direction method of multipliers.