Abstract
Regression-based Super-Resolution (SR) addresses the upscaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this complex non-linear mapping can be properly modeled by a set of linear regressors distributed across the manifold. In such methods, most of the testing time is spent searching for the right regressor within this trained set. In this paper we propose a novel inverse-search approach for regression-based SR. Instead of performing a search from the image to the dictionary of regressors, the search is done inversely from the regressors’ dictionary to the image patches. We approximate this framework by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Additionally, we propose an improved training scheme for SR linear regressors which improves perceived and objective quality. By merging both contributions we improve speed and quality compared to the state-of-the-art.
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References
Tsai, R., Huang, T.: Multiple frame image restoration and registration. In: Proceedings of the Advances in Computer Vision and Image Processing, vol. 1, pp. 317–339 (1984)
Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP Graph. Models Image Process. 53, 231–239 (1991)
Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167–1183 (2002)
Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans. Pattern Anal. Mach. Intell. 26, 83–97 (2004)
Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Trans. Comput. Graph. Appl. 22, 56–65 (2002)
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: Proceedings of the IEEE International Conference on Computer Vision (2009)
Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 30, 12:1–12:11 (2011)
Yang, J., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861–2873 (2010)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparserepresentations. In: Proceedings of the International Conference on Curves and Surfaces (2012)
Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. (2004)
Timofte, R., Smet, V.D., Goool, L.V.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision (2013)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54, 4311–4322 (2006)
Peyré, G.: Manifold models for signals and images. Comput. Vis. Image Underst. 113, 249–260 (2009)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: Spherical hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, STOC 1998, pp. 604–613 (1998)
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval (2010)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing (2008)
He, K., Sun, J.: Computing nearest-neighbor fields via propagation-assisted kd-trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, pp. 1–10 (2012)
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Pérez-Pellitero, E., Salvador, J., Torres-Xirau, I., Ruiz-Hidalgo, J., Rosenhahn, B. (2015). Fast Super-Resolution via Dense Local Training and Inverse Regressor Search. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_23
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DOI: https://doi.org/10.1007/978-3-319-16811-1_23
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