Compressed sensing-enabled super-resolution fluorescence microscopy: a comprehensive review

By overcoming the diffraction limit of light, super-resolution fluorescence microscopy has become an indispensable tool for visualizing subcellular structures and dynamic biological processes. Nevertheless, conventional super-resolution techniques generally require extensive spatial or temporal sampling, resulting in prolonged acquisition times, increased photobleaching and phototoxicity, and limited imaging throughput. Compressed sensing, which exploits the inherent sparsity to recover rich information from undersampled measurements, provides an effective computational framework for addressing these limitations. It has significantly improved imaging speed, acquisition efficiency, and overall system performance, thereby enabling new opportunities for observing dynamic biological events. In this review, we present a comprehensive overview of the fundamental principles, methodological developments, and representative applications of compressed sensing-enabled super-resolution fluorescence microscopy. We further compare the strengths and limitations of different strategies, highlight the remaining challenges, and discuss emerging research directions and future opportunities. These techniques are expected to further expand the capabilities of super-resolution microscopy and play an increasingly important role in high-speed, high-throughput, and low-phototoxicity biological imaging.