State-of-the-art approaches for image deconvolution problems, including modern deep learning architectures
M Makarkin, D Bratashov - Micromachines, 2021 - mdpi.com
In modern digital microscopy, deconvolution methods are widely used to eliminate a number
of image defects and increase resolution. In this review, we have divided these methods into …
of image defects and increase resolution. In this review, we have divided these methods into …
[图书][B] Physics-Informed Machine Learning for Computational Imaging
K Monakhova - 2022 - search.proquest.com
A key aspect of many computational imaging systems, from compressive cameras to low
light photography, are the algorithms used to uncover the signal from encoded or noisy …
light photography, are the algorithms used to uncover the signal from encoded or noisy …
[PDF][PDF] Deep learning for fast spatially-varying deconvolution: supplemental materials
To adapt our method to new systems, an accurate forward model is needed in order to
generate simulated measurements for training. If a good forward model for the microscope …
generate simulated measurements for training. If a good forward model for the microscope …