Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver

Abstract

Super-resolution microscopy enables images to be obtained at a resolution higher than that imposed by the diffraction limit of light. Structured illumination microscopy (SIM) is among the fastest super-resolution microscopy techniques currently in use, and it has gained popularity in the field of cytobiology research owing to its low photo-toxicity and widefield modality. In typical SIM, a fluorescent sample is excited by sinusoidal patterns by employing a linear strategy to reconstruct super-resolution images. However, this strategy fails in cases where non-sinusoidal illumination patterns are used. In this study, we propose the least-squares SIM (LSQ-SIM) approach, which is an efficient super-resolution reconstruction algorithm in the framework of least-squares regression that can process raw SIM data under both sinusoidal and non-sinusoidal illuminations. The results obtained in this study indicate the potential of LSQ-SIM for use in structured illumination microscopy and its various application fields.