IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS

Ana Carolina Correia Rézio, William Robson Schwartz, Helio Pedrini

2012

Abstract

There is currently a growing demand for high-resolution images and videos in several domains of knowledge, such as surveillance, remote sensing, medicine, industrial automation, microscopy, among others. High resolution images provide details that are important to tasks of analysis and visualization of data present in the images. However, due to the cost of high precision sensors and the limitations that exist for reducing the size of the image pixels in the sensor itself, high-resolution images have been acquired from super-resolution methods. This work proposes a method for super-resolving a sequence of images from the compensation residual learned by the features extracted in the residual image and the training set. The results are compared with some methods available in the literature. Quantitative and qualitative measures are used to compare the results obtained with super-resolution techniques considered in the experiments.

References

  1. Baker, S. and Kanade, T. (2002). Limits on SuperResolution and How to Break Them. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1167-1183.
  2. Bascle, B., Blake, A., and Zisserman, A. (1996). Motion Deblurring and Super-resolution from an Image Sequence. In Fourth European Conference on Computer Vision, pages 573-582. Springer-Verlag.
  3. Borman, S. and Stevenson, R. L. (1998). Super-Resolution from Image Sequences - A Review. In Midwest Symposium on Circuits and Systems, pages 374-378.
  4. Chang, H., Yeung, D.-Y., and Xiong, Y. (2004). SuperResolution through Neighbor Embedding. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 275-282.
  5. Chaudhuri, S. (2001a). Super-Resolution Imaging. The Springer International Series in Engineering and Computer Science.
  6. Chaudhuri, S. (2001b). Super-Resolution Imaging. Kluwer Academic Publishers.
  7. Farsiu, S., Robinson, D., Elad, M., and Milanfar, P. (2004a). Advances and Challenges in Super-Resolution. International Journal of Imaging Systems and Technology, 14:47-57.
  8. Farsiu, S., Robinson, M. D., Elad, M., and Milanfar, P. (2004b). Fast and Robust Multiframe Super Resolution. IEEE Transactions on Image Processing, 13(10):1327-1344.
  9. Gonzalez, R. and Woods, R. (2007). Digital Image Processing. Prentice Hall.
  10. Irani, M. and Peleg, S. (1991). Improving Resolution by Image Registration. Graphical Models and Image Processing, 53(3):231-239.
  11. Lin, F. C., Fookes, C. B., Chandran, V., and Sridharan, S. (2005). Investigation into Optical Flow Super-Resolution for Surveillance Applications. In APRS Workshop on Digital Image Computing: Pattern Recognition and Imaging for Medical Applications, Brisbane, Australia.
  12. Liu, X., Song, D., Dong, C., and Li, H. (2008). MAP-Based Image Super-Resolution Reconstruction. In Proceedings of World Academy of Science, pages 208-211.
  13. Lowe, D. (2004). Distinctive Image Features from ScaleInvariant Keypoints. International Journal of Computer Vision, 60:91-110.
  14. Lucien, W. (1999). Definitions and Terms of Reference in Data Fusion. IEEE Transactions on Geosciences and Remote Sensing, 37(3):1190-1193.
  15. Lucien, W., Ranchin, T., and Mangolini, M. (1997). Fusion of Satellite Images of Different Spatial Resolutions: Assessing the Quality of Resulting Images. Photogrammetric Engineering & Remote Sensing, 63:691-699.
  16. Nagel, H.-H. (2011). Image Sequence Server. Institut für Algorithmen und Kognitive Systeme, Universität Karlsruhe. http://i21www.ira.uka.de/image sequences/.
  17. Park, S. C., Park, M. K., and Kang, M. G. (2003). Super-Resolution Image Reconstruction: A Technical Overview. IEEE Signal Processing Magazine, 20(3):21-36.
  18. Patti, A. J. and Altunbasak, Y. (2001). Artifact Reduction for Set Theoretic Super Resolution Image Reconstruction with Edge Adaptive Constraints and HigherOrder Interpolants. IEEE Transactions on Image Processing, 10(1):179-186.
  19. Schwartz, W., da Silva, R., Davis, L., and Pedrini, H. (2011). A Novel Feature Descriptor Based on the Shearlet Transform. In IEEE International Conference on Image Processing, Brussels, Belgium.
  20. Stark, H. (1988). Theory of Convex Projection and its Application to Image Restoration. IEEE International Symposium on Circuits and Systems, pages 963-964.
  21. Sun, J., Sun, J., Xu, Z., and Shum, H.-Y. (2008). Image Super-resolution using gradient profile prior. IEEE Conference on Computer Vision and Pattern Recognition.
  22. Sun, J., Sun, J., Xu, Z., and Shum, H.-Y. (2010). Gradient Profile Prior and Its Applications in Image SuperResolution and Enhancement. IEEE Transactions on Image Processing.
  23. Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612.
  24. Yu, H., Xiang, M., Hua, H., and Chun, Q. (2008). Face Image Super-Resolution through POCS and Residue Compensation. IET Conference Publications, pages 494-497.
Download


Paper Citation


in Harvard Style

Carolina Correia Rézio A., Robson Schwartz W. and Pedrini H. (2012). IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 135-144. DOI: 10.5220/0003861701350144


in Bibtex Style

@conference{visapp12,
author={Ana Carolina Correia Rézio and William Robson Schwartz and Helio Pedrini},
title={IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={135-144},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003861701350144},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS
SN - 978-989-8565-03-7
AU - Carolina Correia Rézio A.
AU - Robson Schwartz W.
AU - Pedrini H.
PY - 2012
SP - 135
EP - 144
DO - 10.5220/0003861701350144