Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution

Abstract

Structured illumination microscopy relies on reconstruction algorithms to yield super-resolution images. Artifacts can arise in the reconstruction and affect the image quality. Current reconstruction methods involve a parametrized apodization function and a Wiener filter. Empirically tuning the parameters in these functions can minimize artifacts, but such an approach is subjective and produces volatile results. We present a robust and objective method that yields optimal results by two straightforward filtering steps with Richardson-Lucy-based deconvolutions. We provide a resource to identify artifacts in 2D-SIM images by analyzing two main reasons for artifacts, out-of-focus background and a fluctuating reconstruction spectrum. We show how the filtering steps improve images of test specimens, microtubules, yeast and mammalian cells.

DMD-based super-resolution structured illumination microscopy visualizes live cell dynamics at high speed and low cost

“…First, we subtracted background signal from the raw data in fairSIM in order to get rid of unwanted background contributions. To further enhance the contrast, fairSIM also allows us to apply Richardson-Lucy deconvolution to the input and output data [36]. Finally, by applying Hessian denoising to the reconstructed data it is possible to further smoothen the fluorescence signal and further enhance the contrast although some resolution improvement will be lost (parameters: µ = 100, σ = 0.8) [37].…”

Deep learning enables structured illumination microscopy with low light levels and enhanced speed

“…Since it was first introduced by the laboratories of Heintzmannl 1 and Gustafsson 2 two decades ago, SIM has been evolving constantly to improve speed, resolution, and to decrease the required light dosages. Reconstruction algorithms have been developed to estimate microscope parameters robustly 3 , minimize reconstruction artifacts 4,5 , reduce the required number of raw images 6 , and check the quality of the raw data and reconstruction 7 . The primary limitation of SIM is the need to obtain a series of highquality images for each reconstructed high-resolution SIM image; this decreases temporal resolution and increases photobleaching.…”

Deep learning enables structured illumination microscopy with low light levels and enhanced speed

“…Since it was first introduced by Mats Gustafsson in 2000 1 , SIM has been evolving constantly to improve speed, resolution and to decrease the required light dosages. Reconstruction algorithms have been developed to estimate microscope parameters robustly 2 , minimize reconstruction artifacts 3,4 , reduce the required number of raw images 5 , and check the quality of the raw data and reconstruction 6 . The primary limitation of SIM is the need to obtain a series of high-quality images for each reconstructed high-resolution SIM image; this decreases temporal resolution and increases photobleaching.…”