By Peyman Milanfar
This e-book features because the definitive assessment of the sphere of super-resolution imaging. Written through the major researchers within the box of photograph and video super-resolution, it surveys the newest cutting-edge options in super-resolution imaging. every one exact bankruptcy offers assurance of the implementations and purposes of super-resolution imaging. Its 14 sections span quite a lot of smooth super-resolution imaging suggestions and comprises variational, Bayesian, feature-based, multi-channel, learning-based, in the community adaptive, and nonparametric equipment. It discusses, between others, scientific, army, and remote-sensing purposes. The booklet can be utilized as a reference, a foundation for short-courses at the topic, or as a part of a graduate direction on electronic photograph processing.
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Extra info for Super-Resolution Imaging
Example-based regularization deployed to super-resolution reconstruction of a single image. The Computer Journal, 52(1):15–30, 2007. 26 Super-Resolution Imaging  M. Elad and A. Feuer. Restoration of single super-resolution image from several blurred, noisy and down-sampled measured images. IEEE Transaction on Image Processing, 6(12):1646–1658, 1997.  M. Elad and Y. Hel-Or. A fast super-resolution reconstruction algorithm for pure translational motion and common space invariant blur. IEEE Transactions on Image Processing, 10(8):1187–1193, 2001.
In Proceedings of Advances in Neural Information Processing Systems, pages 689–695, 2001.  L. J. Yen. On non-uniform sampling of bandwidth limited signals. IRE Transactions on Circuits Theory, 3(4):251–257, 1956.  D. C. Youla and H. Webb. Image registration by the method of convex projections: Part 1-thoery. IEEE Transactions on Medical Imaging, 1(2):81–94, 1982.  D. M. Young. Iterative solution of large linear systems. New York: Academic, 1971. Image Super-Resolution: Historical Overview and Future Challenges 33  W.
One criticism with the aforementioned methods with direct examples is that operating on local patches cannot guarantee global optimality of the estimation. Another kind of example-based approach seeks to perform MAP estimation with local priors on the image space sampled from examples. The pioneering work by Baker and Kanade  formulated an explicit regularization that demands proximity between the spatial derivatives of the unknown image to those of the found examples. The examples are formed by a pyramid derivative set of features, instead of raw data directly.