120 citations · 208 across the 23 of their papers we have counts for
8 papers · 1 filter
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…
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…
Single image dehazing for a variety of haze scenarios using back projected pyramid network
Ayush Singh, Ajay Bhave, Dilip K. Prasad
Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this probl…
High space-bandwidth in quantitative phase imaging using partially spatially coherent optical coherence microscopy and deep neural network
Ankit Butola, Sheetal Raosaheb Kanade, Sunil Bhatt +7
Quantitative phase microscopy (QPM) is a label-free technique that enables to monitor morphological changes at subcellular level. The performance of the QPM system in terms of spat…
Neural network based country wise risk prediction of COVID-19
Ratnabali Pal, Arif Ahmed Sekh, Samarjit Kar +1
The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful…