20 citations · 78 across the 13 of their papers we have counts for
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Artefact removal in ground truth and noise model deficient sub-cellular nanoscopy images using auto-encoder deep learning
Suyog Jadhav, Sebastian Acuña, Krishna Agarwal +1
Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise…
Soft thresholding schemes for multiple signal classification algorithm
Sebastian Acuña, Ida S. Opstad, Fred Godtliebsen +2
Multiple signal classification algorithm (MUSICAL) exploits temporal fluctuations in fluorescence intensity to perform super-resolution microscopy by computing the value of a super…
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells
Arif Ahmed Sekh, Ida S. Opstad, Rohit Agarwal +5
In many real-world scientific problems, generating ground truth (GT) for supervised learning is almost impossible. The causes include limitations imposed by scientific instrument,…
Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs
Rohit Agarwal, Arif Ahmed Sekh, Krishna Agarwal +1
Streaming classification methods assume the number of input features is fixed and always received. But in many real-world scenarios demand is some input features are reliable while…
Silicon substrate significantly alters dipole-dipole resolution in coherent microscope
Zicheng Liu, Krishna Agarwal
Influences of a substrate below samples in imaging performances are studied by reaching the solution to the dyadic Green's function, where the substrate is modeled as half space in…
Fluorescence fluctuations-based super-resolution microscopy techniques: an experimental comparative study
Ida S. Opstad, Sebastian Acuña, Luís Enrique Villegas Hernandez +4
Fluorescence fluctuations-based super-resolution microscopy (FF-SRM) is an emerging field promising low-cost and live-cell compatible imaging beyond the resolution of conventional…