HiFi-SIM: reconstructing high-fidelity structured illumination microscope images
Wen, G.; li, s.; Wang, L.; Chen, X.; Sun, Z.; Liang, Y.; Jin, X.; Tang, Y.; Li, H.
Show abstract
Structured illumination microscopy (SIM) has been a widely-used super-resolution (SR) fluorescence microscopy technique, but artifacts often appear in reconstructed SR images which reduce its fidelity and might cause misinterpretation of biological structures. We present HiFi-SIM, a high-fidelity SIM reconstruction algorithm, by engineering the effective point spread function (PSF) into an ideal form. HiFi-SIM can effectively reduce commonly-seen artifacts without loss of fine structures and improve the axial sectioning. Since results of HiFi-SIM are not sensitive to used PSF and reconstruction parameters, it lowers the requirements for dedicated PSF calibration and complicated parameter adjustment, thus promoting SIM as a daily imaging tool.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Ultra-thin fluorocarbon foils optimize multiscale imaging of three-dimensional native and optically cleared specimens 97%
- 96 Eyes: Parallel Fourier Ptychographic Microscopy for high throughput screening 95%
- An evaluation of multi-excitation-wavelength standing-wave fluorescence microscopy (TartanSW) to improve sampling density in studies of the cell membrane and cytoskeleton 94%
Similar papers in this journal
Similar papers in this journal
- LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm 95%
- An Open-Hardware sample mounting solution for inverted light-sheet microscopes with large detection objective lenses 95%
- Robust optical autofocus system utilizing neural networks trained for extended range and time-course and automated multiwell plate imaging including single molecule localization microscopy 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.