Variational Depth Superresolution Using Example-Based Edge Representations

In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth superresolution approaches try to use additional high resolution intensity images a...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings / IEEE International Conference on Computer Vision pp. 513 - 521
Main Authors: Ferstl, David, Ruther, Matthias, Bischof, Horst
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.12.2015
Subjects:
Coding
Computer vision
Conferences
Dictionaries
Encoding
Energy resolution
Image edge detection
Image reconstruction
Mathematical models
Optimization
Spatial resolution
ISSN:2380-7504
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth superresolution approaches try to use additional high resolution intensity images as guidance for superresolution. In our method we learn a dictionary of edge priors from an external database of high and low resolution examples. In a novel variational sparse coding approach this dictionary is used to infer strong edge priors. Additionally to the traditional sparse coding constraints the difference in the overlap of neighboring edge patches is minimized in our optimization. These edge priors are used in a novel variational superresolution as anisotropic guidance of the higher order regularization. Both the sparse coding and the variational superresolution of the depth are solved based on a primal-dual formulation. In an exhaustive numerical and visual evaluation we show that our method clearly outperforms existing approaches on multiple real and synthetic datasets.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:2380-7504
DOI:10.1109/ICCV.2015.66