References

See here for a list of references

bezanson2017julia
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
William Hadley Richardson, Bayesian-Based Iterative Method of Image Restoration$\ast$, J. Opt. Soc. Am., 62(1), 55–59, 1972.
lucy:74
L.~B. Lucy, {An iterative technique for the rectification of observed distributions}, Astronomical Journal, 79, 745, 1974.
wiener2013extrapolation
Kruse_2017_ICCV
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
Matteo Frigo, Steven~G. Johnson, The Design and Implementation of FFTW3, Proceedings of the IEEE, 93(2), 216–231, 2005.
mogensen2018optim
Patrick Kofod Mogensen, Asbjørn Nilsen Riseth, Optim: A mathematical optimization package for Julia, Journal of Open Source Software, 3(24), 615, 2018.
besard2018juliagpu
Tim Besard, Christophe Foket, Bjorn De Sutter, Effective Extensible Programming: Unleashing Julia on GPUs, arXiv:1712.03112 [cs.PL].
Zygote.jl-2018
Michael Innes, Don't Unroll Adjoint: Differentiating SSA-Form Programs, arXiv:1810.07951 [].
LBFGS
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
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
I. J. Good, R. A. Gaskins, Nonparametric Roughness Penalties for Probability Densities, Biometrika, 58(2), 255–277, 1971.
jax2018github
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
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
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
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
Rainer Heintzmann, Estimating missing information by maximum likelihood deconvolution, Micron, 38(2), 136-144, 2007.
Mertz:2019
Jerome Mertz, Introduction to Optical Microscopy, Cambridge University Press, 2019.
michael_abbott_2021_5047410
Michael Abbott, Dilum Aluthge, N3N5, Simeon Schaub, Carlo Lucibello, Chris Elrod, Johnny Chen, {mcabbott/Tullio.jl: v0.3.0}, 2021.
besard2019prototyping
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
Jiahao Chen, Jarrett Revels, {Robust benchmarking in noisy environments}, arXiv:1608.04295 [cs.PF].
DifferentialEquations.jl-2017
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
Tim Besard, Christophe Foket, Bjorn De Sutter, Effective Extensible Programming: Unleashing Julia on GPUs, IEEE Transactions on Parallel and Distributed Systems, 2018.