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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Vydáno v:	IEEE transactions on image processing Ročník 25; číslo 1; s. 428 - 438
Hlavní autoři:	Jun Xie, Feris, Rogerio Schmidt, Ming-Ting Sun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2016
Témata:	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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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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Cites_doi	10.1109/TPAMI.2006.200 10.1109/TIP.2014.2329766 10.1109/TMM.2012.2232646 10.1109/CVPR.2010.5540206 10.1023/A:1014573219977 10.1109/ICCV.2013.127 10.1109/TPAMI.2004.47 10.1145/2047196.2047270 10.1109/ICCV.2009.5459271 10.1109/ICME.2012.30 10.1109/ICCV.2011.6126423 10.1109/CVPR.2011.5995401 10.1109/ICCPHOT.2010.5585097 10.1109/CVPR.2007.383211 10.1109/CVPR.2013.149 10.1007/978-3-319-11752-2_3 10.1109/CVPR.2014.431 10.1109/VCIP.2013.6706444 10.1109/TPAMI.2010.161 10.1109/ICIP.2014.7025766 10.1109/ICASSP.2011.5946547 10.1109/TIP.2010.2050625 10.1109/TMM.2015.2457678 10.1109/ICME.2014.6890325 10.1109/CVPR.2009.5206804 10.1109/38.988747 10.1109/TPAMI.2010.25 10.1145/1276377.1276497 10.1109/CVPR.2012.6248074 10.1145/1531326.1531330 10.1109/CVPR.2015.7299156 10.1109/CVPR.2013.29
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References	ref35 ref13 ref12 ref37 ref15 ref31 ref30 rajagopalan (ref17) 2008 ref33 ref11 ref32 ref10 ref2 ref1 ref39 zeyde (ref5) 2010 ref16 ref19 barnes (ref36) 2009; 28 ref18 dong (ref9) 2014 felzenszwalb (ref34) 2004 diebel (ref27) 2005 aodha (ref26) 2012 ref24 ref23 ref25 ref20 ref22 scharstein (ref38) 2014 ref21 kim (ref7) 2010; 32 ref28 ref29 ref8 ref4 ref3 tai (ref14) 2006 wang (ref6) 2012 ref40
References_xml	– ident: ref35 doi: 10.1109/TPAMI.2006.200 – ident: ref25 doi: 10.1109/TIP.2014.2329766 – ident: ref8 doi: 10.1109/TMM.2012.2232646 – ident: ref13 doi: 10.1109/CVPR.2010.5540206 – ident: ref37 doi: 10.1023/A:1014573219977 – ident: ref23 doi: 10.1109/ICCV.2013.127 – start-page: 304 year: 2008 ident: ref17 article-title: Resolution enhancement of PMD range maps publication-title: Proc 30th DAGM Symp Pattern Recognit – ident: ref32 doi: 10.1109/TPAMI.2004.47 – ident: ref18 doi: 10.1145/2047196.2047270 – ident: ref11 doi: 10.1109/ICCV.2009.5459271 – ident: ref3 doi: 10.1109/ICME.2012.30 – ident: ref24 doi: 10.1109/ICCV.2011.6126423 – start-page: 291 year: 2005 ident: ref27 article-title: An application of Markov random fields to range sensing publication-title: Proc NIPS – ident: ref10 doi: 10.1109/CVPR.2011.5995401 – start-page: 71 year: 2012 ident: ref26 article-title: Patch based synthesis for single depth image super-resolution publication-title: Proc 12th Eur Conf Comput Vis (ECCV) – ident: ref15 doi: 10.1109/ICCPHOT.2010.5585097 – ident: ref19 doi: 10.1109/CVPR.2007.383211 – ident: ref28 doi: 10.1109/CVPR.2013.149 – start-page: 1948 year: 2006 ident: ref14 article-title: Perceptually-inspired and edge-directed color image super-resolution publication-title: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) – start-page: 31 year: 2014 ident: ref38 article-title: High-resolution stereo datasets with subpixel-accurate ground truth publication-title: Proc German Conf Pattern Recognit doi: 10.1007/978-3-319-11752-2_3 – ident: ref29 doi: 10.1109/CVPR.2014.431 – ident: ref20 doi: 10.1109/VCIP.2013.6706444 – ident: ref33 doi: 10.1109/TPAMI.2010.161 – ident: ref1 doi: 10.1109/ICIP.2014.7025766 – ident: ref40 doi: 10.1109/ICASSP.2011.5946547 – start-page: 184 year: 2014 ident: ref9 article-title: Learning a deep convolutional network for image super-resolution publication-title: Proc 12th Eur Conf Comput Vis (ECCV) – ident: ref4 doi: 10.1109/TIP.2010.2050625 – ident: ref31 doi: 10.1109/TMM.2015.2457678 – start-page: 711 year: 2010 ident: ref5 article-title: On single image scale-up using sparse-representations publication-title: Proc 7th Int Conf Curves Surf – ident: ref30 doi: 10.1109/ICME.2014.6890325 – ident: ref16 doi: 10.1109/CVPR.2009.5206804 – ident: ref2 doi: 10.1109/38.988747 – volume: 32 start-page: 1127 year: 2010 ident: ref7 article-title: Single-image super-resolution using sparse regression and natural image prior publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2010.25 – year: 2004 ident: ref34 article-title: Distance transforms of sampled functions – start-page: 2216 year: 2012 ident: ref6 article-title: Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis publication-title: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) – ident: ref21 doi: 10.1145/1276377.1276497 – ident: ref39 doi: 10.1109/CVPR.2012.6248074 – volume: 28 year: 2009 ident: ref36 article-title: PatchMatch: A randomized correspondence algorithm for structural image editing publication-title: ACM Trans Graph doi: 10.1145/1531326.1531330 – ident: ref12 doi: 10.1109/CVPR.2015.7299156 – ident: ref22 doi: 10.1109/CVPR.2013.29
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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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