Abstract
Super-resolution (SR) techniques produce a high resolution (HR) image from a set of low-resolution (LR) undersampled images. Usually, SR problems are posed as estimation problems where the LR images are contaminated by stationary noise. However, in real SR problems is very common to have non-stationary noise due to problems in the registration of the images or outliers. SR methods that address this type of problems are called robust. In this paper we propose a novel robust SR method that employs a median filter directly in the data from the LR images, before proceeding to the interpolation and deblurring steps that are common in SR. We compare this new method with other robust SR methods with synthetic and real data, proving that it outperforms the other methods in both cases.
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References
Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 20(3), 21–36 (2003)
Farsiu, S., Robinson, D., Elad, M., Milanfar, P.: Advances and challenges in super-resolution. International Journal of Imaging Systems and Technology 14(2), 47–57 (2004)
Schultz, R., Stevenson, R.: Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 5(6), 996–1011 (1996)
Elad, M., Feuer, A.: Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing 6(12), 1646–1658 (1997)
Lertrattanapanich, S., Bose, N.K.: High resolution image formation from low resolution frames using Delaunay triangulation. IEEE Transactions on Image Processing 11(12), 1427–1441 (2002)
Zomet, A., Rav-Acha, A., Peleg, S.: Robust super-resolution. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 (2001)
Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Transactions on Image Processing 13(10), 1327–1344 (2004)
Sánchez-Beato, A., Pajares, G.: Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image. IEEE Transactions on Image Processing 17(10), 1817–1826 (2008)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision (1998)
Bergen, J.R., Anandan, P., Hanna, K.J., Hingorani, R.: Hierarchical model-based motion estimation. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 237–252. Springer, Heidelberg (1992)
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Sánchez-Beato, A., Pajares, G. (2009). Robust Super-Resolution Using a Median Filter for Irregular Samples. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2009. Lecture Notes in Computer Science, vol 5524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02172-5_39
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DOI: https://doi.org/10.1007/978-3-642-02172-5_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02171-8
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