Science Advances

Supplementary Materials

This PDF file includes:

  • Supplementary Materials and Methods
  • Fig. S1. The workflow of inverse deconvolution and frequency truncation for log-normal intensity distribution with 5000 and 2000 (mean and SD) and 350 and 70.
  • Fig. S2. Surrounding emitter deduction to recover the central emitter within the region of interest.
  • Fig. S3. The performance of different algorithms for the simulated dataset with extremely dim emitters.
  • Fig. S4. The performance of different algorithms for the simulated dataset with dim emitters and nonuniform background.
  • Fig. S5. The performance of different algorithms for the open-access experimental high-density localization dataset.
  • Fig. S6. The performance of different algorithms for reconstructing super-resolution images from experimental dataset of imaging mEos3.2-labeled vimentin.
  • Fig. S7. The performance of different algorithms for reconstructing super-resolution images from experimental dataset of imaging mEos3.2-labeled endoplasmic reticulum.
  • Fig. S8. The performance of different algorithms for reconstructing super-resolution images from experimental dataset of imaging nucleosomes labeled with Alexa Fluor 647.
  • Fig. S9. The effect of mismatched PSF width (σ) between WindSTORM and the actual dataset using both simulated and experimental dataset.
  • Fig. S10. Relationship between the temporal minima value to the expected background value.

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