References
See here for a list of references
- bezanson2017julia
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Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B Shah, Julia: A fresh approach to numerical computing, SIAM review, 59(1), 65–98, 2017.
- Richardson:72
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William Hadley Richardson, Bayesian-Based Iterative Method of Image Restoration$\ast$, J. Opt. Soc. Am., 62(1), 55–59, 1972.
- lucy:74
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L.~B. Lucy, {An iterative technique for the rectification of observed distributions}, Astronomical Journal, 79, 745, 1974.
- wiener2013extrapolation
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Nobert Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications, Wiley, 1949.
- Kruse_2017_ICCV
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Jakob Kruse, Carsten Rother, Uwe Schmidt, Learning to Push the Limits of Efficient FFT-Based Image Deconvolution, In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
- FFTW05
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Matteo Frigo, Steven~G. Johnson, The Design and Implementation of FFTW3, Proceedings of the IEEE, 93(2), 216–231, 2005.
- mogensen2018optim
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Patrick Kofod Mogensen, Asbjørn Nilsen Riseth, Optim: A mathematical optimization package for Julia, Journal of Open Source Software, 3(24), 615, 2018.
- besard2018juliagpu
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Tim Besard, Christophe Foket, Bjorn De Sutter, Effective Extensible Programming: Unleashing Julia on GPUs, arXiv:1712.03112 [cs.PL].
- Zygote.jl-2018
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Michael Innes, Don't Unroll Adjoint: Differentiating SSA-Form Programs, arXiv:1810.07951 [].
- LBFGS
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Dong C. Liu, Jorge Nocedal, On the Limited Memory BFGS Method for Large Scale Optimization, Math. Program., 45(1–3), 503–528, 1989.
- Verveer:98
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Peter J. Verveer, Thomas M. Jovin, Image restoration based on Good's roughness penalty with application to fluorescence microscopy, J. Opt. Soc. Am. A, 15(5), 1077–1083, 1998.
- Good:71
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I. J. Good, R. A. Gaskins, Nonparametric Roughness Penalties for Probability Densities, Biometrika, 58(2), 255–277, 1971.
- jax2018github
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James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, Qiao Zhang, JAX: composable transformations of Python+NumPy programs, GitHub, 2018.
- deconvlab2
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D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, M. Unser, DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy, Methods—Image Processing for Biologists, 115, 28–41, 2017.
- ikoma2018convex
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Hayato Ikoma, Michael Broxton, Takamasa Kudo, Gordon Wetzstein, A convex 3D deconvolution algorithm for low photon count fluorescence imaging, Scientific reports, 8(1), 11489, 2018.
- CUDA
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John Nickolls, Ian Buck, Michael Garland, Kevin Skadron, Scalable Parallel Programming with CUDA, In ACM SIGGRAPH 2008 Classes, SIGGRAPH '08, New York, NY, USA, 2008. Association for Computing Machinery.
- rel_energy_regain
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Rainer Heintzmann, Estimating missing information by maximum likelihood deconvolution, Micron, 38(2), 136-144, 2007.
- Mertz:2019
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Jerome Mertz, Introduction to Optical Microscopy, Cambridge University Press, 2019.
- michael_abbott_2021_5047410
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Michael Abbott, Dilum Aluthge, N3N5, Simeon Schaub, Carlo Lucibello, Chris Elrod, Johnny Chen, {mcabbott/Tullio.jl: v0.3.0}, 2021.
- besard2019prototyping
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Tim Besard, Valentin Churavy, Alan Edelman, Bjorn De Sutter, Rapid software prototyping for heterogeneous and distributed platforms, Advances in Engineering Software, 132, 29–46, 2019.
- BenchmarkTools.jl-2016
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Jiahao Chen, Jarrett Revels, {Robust benchmarking in noisy environments}, arXiv:1608.04295 [cs.PF].
- DifferentialEquations.jl-2017
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Christopher Rackauckas, Qing Nie, DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia, The Journal of Open Research Software, 5(1), 2017.
- LLVM.jl-2017
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Tim Besard, Christophe Foket, Bjorn De Sutter, Effective Extensible Programming: Unleashing Julia on GPUs, IEEE Transactions on Parallel and Distributed Systems, 2018.