Learning Consistent Discretizations of the Total Variation

In this work, we study a general framework of discrete approximations the total variation for image reconstruction problems. The framework, which can show consistency in sense $\Gamma$-convergence, unifies and extends several existing discretization schemes. addition, propose algorithms learning discretizations order to achieve best possible quality particular tasks. Interestingly, learned significantly differ between tasks, illustrating that there is no universal variation.