Computes the second order mid point model
for manifoldvalued data , where is some given data, that might only be given on a subset of the domain. Then the data term is reduced accordingly.For the prior both the total variation and the second order mid point model are employed. For more details, see [1].
Matlab Documentation
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% gradientSecondOrderLog(M,f,alpha,beta) the l^2TVTV2 mid point model
%
% INPUT
% M : a manifold
% f : given (original) data
% x : value to take the gradient at
% alpha : weight of TV
% beta : weight of TV2
%
% OPTIONAL
% 'p' : (p=1) compute TV with pnorm coupling in the dimensions of
% the data, i.e. anisotropic TV for p=1 and isotropic for p=2
% 'epsilon' : compute the gradient of the epsilonrelaxed TV and TV2
% 
% MVIRT  R. Bergmann, 20171207
See also
 functional for second order total variation midpoint
 functional for total variation
 List of available functionals
References

Bačák, M, Bergmann, R, Steidl, G and Weinmann, A (2016). A second order nonsmooth variational model for restoring manifoldvalued images. SIAM Journal on Scientific Computing. 38 A567–A597
We introduce a new nonsmooth variational model for the restoration of manifoldvalued data which includes second order differences in the regularization term. While such models were successfully applied for realvalued images, we introduce the second order difference and the corresponding variational models for manifold data, which up to now only existed for cyclic data. The approach requires a combination of techniques from numerical analysis, convex optimization and differential geometry. First, we establish a suitable definition of absolute second order differences for signals and images with values in a manifold. Employing this definition, we introduce a variational denoising model based on first and second order differences in the manifold setup. In order to minimize the corresponding functional, we develop an algorithm using an inexact cyclic proximal point algorithm. We propose an efficient strategy for the computation of the corresponding proximal mappings in symmetric spaces utilizing the machinery of Jacobi fields. For the \(n\)sphere and the manifold of symmetric positive definite matrices, we demonstrate the performance of our algorithm in practice. We prove the convergence of the proposed exact and inexact variant of the cyclic proximal point algorithm in Hadamard spaces. These results which are of interest on its own include, e.g., the manifold of symmetric positive definite matrices.@article{BBSW16, author = {Ba{\v{c}}{\'a}k, Miroslav and Bergmann, Ronny and Steidl, Gabriele and Weinmann, Andreas}, title = {A second order nonsmooth variational model for restoring manifoldvalued images}, journal = {SIAM Journal on Scientific Computing}, year = {2016}, volume = {38}, number = {1}, pages = {A567A597}, doi = {10.1137/15M101988X}, eprint = {1506.02409}, eprinttype = {arXiv} }