Sublabel-Accurate Convex Relaxation with Total Generalized Variation Regularization
| Autor: | Bastian Goldluecke, Michael Strecke |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Local flatness
Computer science Small number Convex relaxation 02 engineering and technology First order 01 natural sciences Regularization (mathematics) 010101 applied mathematics Normal field Total generalized variation Normal mapping 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing 0101 mathematics |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030129385 GCPR |
| DOI: | 10.1007/978-3-030-12939-2_19 |
| Popis: | We propose a novel idea to introduce regularization based on second order total generalized variation (\(\text {TGV}\)) into optimization frameworks based on functional lifting. The proposed formulation extends a recent sublabel-accurate relaxation for multi-label problems and thus allows for accurate solutions using only a small number of labels, significantly improving over previous approaches towards lifting the total generalized variation. Moreover, even recent sublabel accurate methods exhibit staircasing artifacts when used in conjunction with common first order regularizers such as the total variation (\(\text {TV}\)). This becomes very obvious for example when computing derivatives of disparity maps computed with these methods to obtain normals, which immediately reveals their local flatness and yields inaccurate normal maps. We show that our approach is effective in reducing these artifacts, obtaining disparity maps with a smooth normal field in a single optimization pass. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|