Spatial Image Resolution Assessment by Fourier Analysis (SIRAF)

Brostrøm, Anders; Mølhave, Kristian

doi:10.1017/s1431927622000228

Cited by 4 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The largest frequencies can either be used to estimate the FWHM of the point-spread function (see refs. 47 , 48 and Methods) or assess the quality of the resulting images 49 , 50 . To estimate whether CARE improves the resolution of the noisy images, we first applied an FFT, calculated the radial average of the resulting power spectrum and fitted to a rationalized equation based on a Gaussian point-spread function to extract d m i n , the minimally resolvable distance 49 .…”

Section: Resultsmentioning

confidence: 99%

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy

et al. 2022

View full text Add to dashboard Cite

Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensitive processes in living cells and animals. The low photon emission of known luciferases, however, demands long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. We apply our method to image subcellular dynamics in mouse embryonic stem cells, epithelial morphology during zebrafish development, and DAF-16 FoxO transcription factor shuttling from the cytoplasm to the nucleus under external stress. Finally, we concatenate neural networks for denoising and light-field deconvolution to resolve intracellular calcium dynamics in three dimensions of freely moving Caenorhabditis elegans.

show abstract

Section: Resultsmentioning

confidence: 99%

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy

et al. 2022

View full text Add to dashboard Cite

show abstract

Wind Speed Downscaling of the WRF Model at Subkilometer Scale in Complex Terrain for Wind Power Applications

Paola,

Cimini,

De Natale

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Mesoscale Numerical Weather Prediction Models (NWPMs) are frequently utilized for wind speed analysis and forecasting in the planning and supporting of wind power generation. However, high computational costs only allow for routine use up to kilometer scale which is sometimes too coarse to support onshore wind power generation in areas with complex orography. To address this, an algorithm was developed in southern Italy to downscale the wind fields output using the Weather Research and Forecasting (WRF) model for the first 250 meters above ground level. The algorithm is based on Artificial Neural Networks (ANNs) and uses the WRF model outputs on a 1.2 km regular grid, and the land surface height and orientation on a 240 m regular grid to downscale wind fields to a 240 m regular grid. To train the ANNs, a WRF simulation dataset in Large Eddy Simulation (LES) mode was developed. Particular attention was paid to defining the ANN architectures and analyzing inputs to mitigate overfitting risk while maintaining manageable computation costs. The evaluation of outcomes conducted using an independent test datasets from WRF-LES simulations reveals that the wind speed Root Mean Square Difference (RMSD) is 0.5 m/s over land and 0.2 m/s over the sea surface, respectively, at a spatial resolution of approximately 800 m. These figures are lower than the RMSD values of 1.6 m/s over land and 1.0 m/s over the sea surface, accompanied by a spatial resolution of 1.8 km, which were obtained through comparison with the Spline interpolation method. 1

show abstract

Seven decades of image super-resolution: achievements, challenges, and opportunities

Maiseli,

Abdalla

2024

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Super-resolution imaging has, for more than seventy years, gradually evolved to produce advanced methods for enhancing the resolution of images beyond the diffraction limits. Notwithstanding its foreseeable practical capabilities, we noted that this technology has received undeserved attention. The present work provides an extensive review of super-resolution imaging since its first conception in 1952, contextualizing studies into four research directions: reviews, advances, applications, and hardware implementations. We have discussed achievements, challenges, and potential opportunities of super-resolution imaging to equip researchers, especially those in their early careers, with knowledge to further advance the technology. Our work may open interesting research avenues that may accelerate realization of the technology into commercial imaging devices.

show abstract

Spatial Image Resolution Assessment by Fourier Analysis (SIRAF)

Cited by 4 publications

References 32 publications

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy

Wind Speed Downscaling of the WRF Model at Subkilometer Scale in Complex Terrain for Wind Power Applications

Seven decades of image super-resolution: achievements, challenges, and opportunities

Contact Info

Product

Resources

About