Faster super-resolution imaging of high density molecules via a cascading algorithm based on compressed sensing

This paper proposes a cascading algorithm (CSR) based on compressed sensing, which aims to reduce intensive computations in super-resolution imaging of fluorescence microscopy. Performance of existing algorithms such as CVX and L1H drop sharply when applied to obtain finer images with high density molecules. CSR fully exploits the extreme sparsity property of molecules in the compressed sensing model and progressively restricts solution space stage by stage. We perform a comprehensive study of existing algorithms and the proposed algorithm under different resolutions and molecules' densities. Simulation and experimental results confirm the performance advantage of CSR when applied to recover dense molecules.