Abstract
In this paper, we propose a variational framework for computing a superresolved image of a scene from an arbitrary input video. To this end, we employ a recently proposed quadratic relaxation scheme for high accuracy optic flow estimation. Subsequently we estimate a high resolution image using a variational approach that models the image formation process and imposes a total variation regularity of the estimated intensity map. Minimization of this variational approach by gradient descent gives rise to a deblurring process with a nonlinear diffusion. In contrast to many alternative approaches, the proposed algorithm does not make assumptions regarding the motion of objects. We demonstrate good experimental performance on a variety of real-world examples. In particular we show that the computed super resolution images are indeed sharper than the individual input images.
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References
Chambolle, A.: An Algorithm for Total Variation Minimization and Applications. J. Math. Imaging Vis. 20, 89–97 (2004)
Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An improved algorithm for TV-L1 optical flow computation. In: Proceedings of the Dagstuhl Visual Motion Analysis Workshop (2008)
Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M., Szeliski, R.: A Database and Evaluation Methodology for Optical Flow, http://vision.middlebury.edu/flow/data/
Elad, E., Feuer, A.: Restoration of single super-resolution image from several blurred, noisy and down-sampled measured images. IEEE Trans. Image Processing 6, 1646–1658 (1997)
Irani, M., Peleg, S.: Improving resolution by image registration. In: CVGIP: Graph. Models Image Process, pp. 231–239 (1991)
Ur, H., Gross, D.: Improved resolution from subpixel shifted pictures. Graphical Models and Image Processing 54, 181–186 (1992)
Farsiu, S., Robinson, M., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Transactions on Image Processing 13, 1327–1344 (2004)
Kim, S., Bose, N., Valenzuela, H.: Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Transactions on Acoustics, Speech and Signal Processing 38, 1013–1027 (1990)
Huang, T., Tsai, R.: Multi-frame image restoration and registration. Advances in Computer Vision and Image Processing 1, 317–339 (1984)
Elad, M., Feuer, A.: Super-resolution reconstruction of image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 817–834 (1999)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Pock, T.: Fast Total Variation for Computer Vision. Graz University of Technology, Austria (2008) (PhD)
Zach, C., Pock, T., Bischof, H.: A Duality Based Approach for Realtime TV-L1 Optical Flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007)
Malinfar, P.: MDSP Super-Resolution And Demosaicing Datasets. University of California, Santa Cruz, http://www.ee.ucsc.edu/~milanfar/software/sr-datasets.html
Wang, C.: Vision and Autonomous Systems Center’s Image Database. Carnegie Mellon University, http://vasc.ri.cmu.edu/idb/html/motion/index.html
Protter, M., Elad, M., Takeda, H., Milanfar, P.: Generalizing the Non-Local-Means to Super-Resolution Reconstruction. IEEE Transactions on Image Processing 18, 36–51 (2009)
Ebrahimi, M., Vrscay, R.: Multi-Frame Super-Resolution with No Explicit Motion Estimation IPCV, pp. 455–459 (2008)
Kelley, C.T.: Iterative Methods for Linear and Nonlinear Equations. SIAM, Philadelphia (1995)
Zomet, A., Peleg, S.: Superresolution from multiple images having arbitrary mutual motion. In: Super-Resolution Imaging, pp. 195–209. Kluwer, Dordrecht (2001)
Marquina, A., Osher, S.J.: Image Super-Resolution by TV-Regularization and Bregman Iteration. Journal of Scientific Computing 37(3) (2008)
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Mitzel, D., Pock, T., Schoenemann, T., Cremers, D. (2009). Video Super Resolution Using Duality Based TV-L 1 Optical Flow. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_44
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DOI: https://doi.org/10.1007/978-3-642-03798-6_44
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