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Reseach Article

A Novel Approach for Super Resolution of Video Frame using Spatially Adaptive Total Variation

by Vinod Kumar Banse, Kamlesh Chandravanshi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 4
Year of Publication: 2017
Authors: Vinod Kumar Banse, Kamlesh Chandravanshi
10.5120/ijca2017915575

Vinod Kumar Banse, Kamlesh Chandravanshi . A Novel Approach for Super Resolution of Video Frame using Spatially Adaptive Total Variation. International Journal of Computer Applications. 176, 4 ( Oct 2017), 29-34. DOI=10.5120/ijca2017915575

@article{ 10.5120/ijca2017915575,
author = { Vinod Kumar Banse, Kamlesh Chandravanshi },
title = { A Novel Approach for Super Resolution of Video Frame using Spatially Adaptive Total Variation },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2017 },
volume = { 176 },
number = { 4 },
month = { Oct },
year = { 2017 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number4/28542-2017915575/ },
doi = { 10.5120/ijca2017915575 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:41:39.121022+05:30
%A Vinod Kumar Banse
%A Kamlesh Chandravanshi
%T A Novel Approach for Super Resolution of Video Frame using Spatially Adaptive Total Variation
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 4
%P 29-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Super resolution (SR) for real-life video sequences is a challenging problem due to complex nature of the motion fields. In this paper, a novel blind SR method is proposed to improve the spatial resolution of video sequences, while the overall point spread function of the imaging system, motion fields, and noise statistics are unknown. The high-resolution frames are estimated using a cost function that has the fidelity and regularization terms of type Huber–Markov random field to preserve edges and fine details. The fidelity term is adaptively weighted at each iteration using a masking operation to suppress artifacts due to inaccurate motions. Very promising results are obtained for real-life videos containing detailed structures, complex motions, fast-moving objects, deformable regions, or severe brightness changes. The proposed method outperforms the state of the art in all performed experiments through both subjective and objective evaluations.

References
  1. Faramarzi, E., Rajan, D., Fernandes, F. C., & Christensen, “Blind Super Resolution of Real-Life Video Sequences”. IEEE Transactions on Image Processing, 25(4), 1544-1555. (2016)
  2. Cho, Sunghyun, Jue Wang, and Seungyong Lee. “Video deblurring for hand-held cameras using patch-based synthesis” ACM Transactions on Graphics (TOG) 31, no. 4 (2012): 64.
  3. Wang, Yi, Ronald Fevig, and Richard R. Schultz. “Super-resolution mosaicking of UAV surveillance video” In Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on, pp. 345-348. IEEE, 2008.
  4. Yoshida, Tomonari, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, and Hiroshi Murase. “Robust face super-resolution using free-form deformations for low-quality surveillance video” In Multimedia and Expo (ICME), 2012 IEEE International Conference on, pp. 368-373. IEEE, 2012.
  5. Camargo, Aldo, Richard Schultz, and Qiang He. “Super-resolution mosaicking of unmanned aircraft system (UAS) surveillance video using Levenberg Marquardt (LM) algorithm” Advances in Visual Computing (2010): 698-706.
  6. S. Ebihara, M. Sato, and H. Niitsuma, “Super-Resolution of Coherent Targets by Directional Borehole Radar” IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 4, pp. 1725_1732, Jul. 2000.
  7. A. Gehani and J. H. Reif, “Super-Resolution Video Analysis for Forensic Investigations” in IFIP WG 11.9 International Conference on Digital Forensics, ser. IFIP, vol. 242. Springer, 2007, pp. 281_299.
  8. Y. Wang, R. Fevig, and R. R. Schultz, “Super-resolution mosaicking of UAV surveillance video” in IEEE International Conference on Image Processing (ICIP), 2008, pp. 345_348.
  9. T. S. Huang and R. Y. Tsai, _Multiframe image restoration and registration,_ Advances in Computer Vision and Image Processing, vol. 1, no. 7, pp. 317_339, 1984
  10. M. Irani and S. Peleg, “Super resolution from image sequences” in 10th Intern. Conf. on Pattern Reco. vol. 2, Jun. 1990, pp. 115_120.
  11. S. Borman and R. Stevenson, “Super-Resolution from Image Sequences-A Review” in Midwest Symposium on Circuits and Systems, Notre Dame, IN, USA, 8 1998, pp. 374_378.
  12. A. Papoulis, “Generalized Sampling Expansion” IEEE Trans. on Circuits and Sys-tems, vol. 24, no. 11, pp. 652_654, Nov. 1977.
  13. N. K. Bose and N. A. Ahuja, “Super resolution and Noise Filtering Using Moving Least Squares” IEEE Trans. on Image Processing, vol. 15, no. 8, pp. 2239_2248, Aug. 2006.
  14. A. J. Patti, M. I. Sezan, and A. M. Tekalp, “Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzero Aperture Time” IEEE Trans. on Image Processing, vol. 6, no. 8, pp. 1064_1076, Aug. 1997.
  15. S. Rhee and M. G. Kang, “DCT-Based Regularized Algorithm for High-Resolution Image Reconstruction” in IEEE International Conference on Image Processing (ICIP). Los Alamitos, CA: IEEE, Oct. 1999, pp. 184_187.
Index Terms

Computer Science
Information Sciences

Keywords

Video super resolution blur de-convolution blind estimation Huber Markov random field (HMRF).