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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Published in:	IEEE transactions on image processing Vol. 25; no. 1; pp. 428 - 438
Main Authors:	Jun Xie, Feris, Rogerio Schmidt, Ming-Ting Sun
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2016
Subjects:	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
Online Access:	Get full text
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Abstract	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.
AbstractList	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. 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.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.
Author	Jun Xie Ming-Ting Sun Feris, Rogerio Schmidt
Author_xml	– sequence: 1 surname: Jun Xie fullname: Jun Xie email: junx@uw.edu organization: Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA – sequence: 2 givenname: Rogerio Schmidt surname: Feris fullname: Feris, Rogerio Schmidt email: rsferis@us.ibm.com organization: IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA – sequence: 3 surname: Ming-Ting Sun fullname: Ming-Ting Sun email: mts@uw.edu organization: Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA
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SubjectTerms	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
Title	Edge-Guided Single Depth Image Super Resolution
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