Dualization and Automatic Distributed Parameter Selection of Total Generalized Variation via Bilevel Optimization

Understand

Total Generalized Variation (TGV) regularization in image reconstruction relies on an infimal convolution type combination of generalized first- and second-order derivatives.

• This helps to avoid the staircasing effect of Total Variation (TV) regularization, while still preserving sharp contrasts in images.
• The associated regularization effect crucially hinges on two parameters whose proper adjustment represents a challenging task.
• In this work, a bilevel optimization framework with a suitable statistics-based upper level objective is proposed in order to automatically select these parameters.