Regularization for Super-Resolution Image Reconstruction
Super-resolution image reconstruction estimates a high-resolution image from a sequence of low-resolution, aliased images. The estimation is an inverse problem and is known to be ill-conditioned, in the sense that small errors in the observed images can cause large changes in the reconstruction. The paper discusses application of existing regularization techniques to super-resolution as an intelligent means of stabilizing the reconstruction process. Some most common approaches are reviewed and experimental results for iterative reconstruction are presented.
KeywordsInverse Problem Singular Value Decomposition Regularization Parameter Regularization Term Image Restoration
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- 1.Chaudhuri, S.: Super-Resolution Imaging, p. 279. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
- 5.Morozov, V.A.: Regularization Methods for Ill-Posed Problems. English (ed.). CRC Press, Boca Raton (1993)Google Scholar
- 6.Tikhonov, A.N. (ed.): Ill-Posed Problems in Natural Sciences. TVP, Sci. Publishers, Moscow (1991)Google Scholar
- 8.Tikhonov, A.N.: Solution of Incorrectly Formulated Problems and the Regularization Method. Soviet Math. Dokl 4, 1035–1038 (1963)Google Scholar
- 9.Hadamard, J.: Sur les problèmes aux dérivées partielles et leur signification physique (On the problems with the derivative partial and their physical significance), pp. 49–52. Princeton University Bulletin (1902)Google Scholar
- 10.Groetsch, C.W.: The Theory of Tikhonov Regulaization for Fredholm Equations of the First Kind. Research Notes in Mathematics, vol. 105. Pitman, Boston (1984)Google Scholar
- 17.Wahba, G.: Spline Model for Observational Data. In: CBMS-NSF regional conference series in applied mathematics, vol. 59, Society for Industrial and Applied Mathematics, Philadelphia (1990)Google Scholar