FAIR: Flexible Algorithms for Image Registration

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Fixed-Point Algorithms for Inverse Problems in Science and Engineering (2011, background): “Another interesting development in the use of modern optimization tools in signal processing was proposed by Modersitzki [68] in the context of regularized variational image registration (see also [69]).”
Medical image registration: a review (2014, methodology): “The well-known demons algorithm and its variations (Thirion 1998; Guimond et al. 2001, 2002; Wang et al. 2005; Vercauteren et al. 2007; 2009; Yeo et al. 2010a; Gooya et al. 2011) are examples of optical ﬂow-based registration algorithms.”
Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation (2011, unknown): “(25)), a positive semi-definite approximation is used, that ignores derivatives of the template that are higher than first order (see eg Modersitzki (2009)).”
Stable architectures for deep neural networks (2017, background): “image registration [39], however, to the best of our knowledge not commonly employed in deep learning.”
Deformable Medical Image Registration: A Survey (2013, methodology): “They employed a discretize-then-optimize approach [9] that involved the partitioning voxels to 24 tetrahedra.”