Total Variation =============== This section deals with an iterative reconstruction algorithm (hence, different from the filtered back projection), that solves the minimization problem :: x = argmin 1/2||H x - y||^2 + beta_tv TV(x) where: * x is the image to be reconstructed * H is the projection matrix * y is the sinogram * TV(x) is the total variation semi-norm of x, that is, the l1 norm of its gradient * beta_tv is a parameter controlling the relative importance of the two terms in the minimization This algorithm tends to reconstruct piecewise-constant images. It is therefore suitable for images with a limited number of phases, such as many images in materials science. It is not suited, however, for images with a very irregular texture, or for images with smooth large-scale gradients. *The following documentation has been extracted automatically from the comments found in the source code. Discard Parameters. object variable.* .. automodule:: Parameters_module :noindex: .. autoclass:: Parameters :members: ITERATIVE_CORRECTIONS,DO_PRECONDITION, FISTA,DENOISING_TYPE,ITERATIVE_CORRECTIONS_NOPREC,BETA_TV, N_ITERS_DENOISING, DUAL_GAP_STOP, OPTIM_ALGORITHM