Edge-Guided Single Depth Image Super Resolution

Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and the quality of the depth map generated by these cameras are still problematic for several applications. In this paper, a novel framework for the single depth image su...

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Veröffentlicht in:IEEE transactions on image processing Jg. 25; H. 1; S. 428 - 438
Hauptverfasser: Jun Xie, Feris, Rogerio Schmidt, Ming-Ting Sun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2016
Schlagworte:
Cameras
Color
Construction
Edge- Guided
Image edge detection
Image processing
Image reconstruction
Image resolution
Joint Bilateral Up-sampling
Joints
Markov Random Field
Preserves
Single Depth Image
Super Resolution
Surface layer
Texture
Training
super resolution
Markov random field
Single depth image
joint bilateral up-sampling
edge-guided
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and the quality of the depth map generated by these cameras are still problematic for several applications. In this paper, a novel framework for the single depth image superresolution is proposed. In our framework, the upscaling of a single depth image is guided by a high-resolution edge map, which is constructed from the edges of the low-resolution depth image through a Markov random field optimization in a patch synthesis based manner. We also explore the self-similarity of patches during the edge construction stage, when limited training data are available. With the guidance of the high-resolution edge map, we propose upsampling the high-resolution depth image through a modified joint bilateral filter. The edge-based guidance not only helps avoiding artifacts introduced by direct texture prediction, but also reduces jagged artifacts and preserves the sharp edges. Experimental results demonstrate the effectiveness of our method both qualitatively and quantitatively compared with the state-of-the-art methods.
Bibliographie:ObjectType-Article-1
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2015.2501749