Imaging Systems

Download Super-Resolution Imaging by Peyman Milanfar PDF

By Peyman Milanfar

ISBN-10: 1439819300

ISBN-13: 9781439819302

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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Example-based regularization deployed to super-resolution reconstruction of a single image. The Computer Journal, 52(1):15–30, 2007. 26 Super-Resolution Imaging [26] 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. [27] 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. [109] L. J. Yen. On non-uniform sampling of bandwidth limited signals. IRE Transactions on Circuits Theory, 3(4):251–257, 1956. [110] 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. [111] D. M. Young. Iterative solution of large linear systems. New York: Academic, 1971. Image Super-Resolution: Historical Overview and Future Challenges 33 [112] 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 [2] 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.

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