Here are the classes, structs, unions and interfaces with brief descriptions:

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Functionals	A collection of functionals for different computer vision/image processing tasks used to demonstrate the use of nmiPiano/iPiano optimization
CompressiveSensing	Compressive sensing example using convex optimization as discussed in [1]: [1] G. Kutyniok. Compressed Sensing: Theory and Applications. Computing Research Repository, abs/1203.3815, 2012
Denoising	Denoising functionals (for grayscale images!
Evaluation	Evaluation of denoising functionals
Noise	Generate noisy images
PhaseField	Two Phase-Field for segmentation, see [1]. [1] Shen. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation. International Conference on Advanced Concepts of Intelligent Vision Systems, 2005
PhaseField_Color	Two Phase-Field for color image segmentation, see [1]; adapted to color. [1] Shen. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation. International Conference on Advanced Concepts of Intelligent Vision Systems, 2005
Util	Utilities for using nmiPiano/iPiano for computer vision/image processing
iPiano	Implementation of iPiano (algorithm 5) as proposed in [1]: [1] P. Ochs, Y. Chen, T. Brox, T. Pock. iPiano: Inertial Proximal Algorithm for Nonconvex Optimization. SIAM J. Imaging Sciences, vol. 7, no. 2, 2014
Iteration	Structure representing an iteration, passed as is to a callback function to be able to monitor process
Options	Options of algorithm
nmiPiano	Implementation of nmiPiano (algorithm 4) as proposed in [1]: [1] P. Ochs, Y. Chen, T. Brox, T. Pock. iPiano: Inertial Proximal Algorithm for Nonconvex Optimization. SIAM J. Imaging Sciences, vol. 7, no. 2, 2014
Iteration	Structure representing an iteration, passed as is to a callback function to be able to monitor process
Options	Options of algorithm