Fetching the paper…
Reading the bibliography…
Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing.
“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2014
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
Earlier work this paper cites.
“Content-aware image restoration: pushing the limits of fluorescence microscopy,”
Martin Weigert, Uwe Schmidt, Tobias Boothe, Andreas Müller, Alexandr Dibrov, Akanksha Jain, Benjamin Wilhelm, Deborah Schmidt, Coleman Broaddus, Siân Culley, et al., · 2018
Earlier work this paper cites.
“Differential lateral and basal tension drive folding of drosophila wing discs through two distinct mechanisms,”
Liyuan Sui, Silvanus Alt, Martin Weigert, Natalie Dye, Suzanne Eaton, Florian Jug, Eugene W Myers, Frank Jülicher, Guillaume Salbreux, and Christian Dahmann, · 2018
Earlier work this paper cites.
“Deep learning massively accelerates super-resolution localization microscopy,”
Wei Ouyang, Andrey Aristov, Mickaël Lelek, Xian Hao, and Christophe Zimmer, · 2018
Earlier work this paper cites.
“Noise2Noise: Learning image restoration without clean data,”
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila, · 2018
Cited alongside, same era.
“A poisson-gaussian denoising dataset with real fluorescence microscopy images,”
Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Siyuan Zhang, Cody Smith, and Scott Howard, · 2019
Cited alongside, same era.
“Cryo-care: content-aware image restoration for cryo-transmission electron microscopy data,”
Tim-Oliver Buchholz, Mareike Jordan, Gaia Pigino, and Florian Jug, · 2019
Cited alongside, same era.
“Content-aware image restoration for electron microscopy,”
Tim-Oliver Buchholz, Alexander Krull, Réza Shahidi, Gaia Pigino, Gáspár Jékely, and Florian Jug, · 2019
Cited alongside, same era.
“Noise2void-learning denoising from single noisy images,”
Alexander Krull, Tim-Oliver Buchholz, and Florian Jug, · 2019
Cited alongside, same era.
“Probabilistic noise2void: Unsupervised content-aware denoising,”
Alexander Krull, Tomas Vicar, and Florian Jug, · 2019
Closest in time.
“Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,”
Chinmay Belthangady and Loic A Royer, · 2019
Closest in time.
“Nanoj: a high-performance open-source super-resolution microscopy toolbox,”
Romain F Laine, Kalina L Tosheva, Nils Gustafsson, Robert DM Gray, Pedro Almada, David Albrecht, Gabriel T Risa, Fredrik Hurtig, Ann-Christin Lindås, Buzz Baum, et al., · 2019
Closest in time.
“Noise2self: Blind denoising by self-supervision,”
Joshua Batson and Loic Royer, · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…