Image Super-Resolution by TV-Regularization and Bregman Iteration
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In this paper we formulate a new time dependent convolutional model for super-resolution based on a constrained variational model that uses the total variation of the signal as a regularizing functional. We propose an iterative refinement procedure based on Bregman iteration to improve spatial resolution. The model uses a dataset of low resolution images and incorporates a downsampling operator to relate the high resolution scale to the low resolution one. We present an algorithm for the model and we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme and quality of the results.
KeywordsSuper-resolution Total variation restoration Bregman iteration Downsampling Edge preserving
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- 5.Chaudhuri, S.: Super-Resolution Imaging. Kluwer Academic, Dordrecht (2001) Google Scholar
- 11.Marquina, A.: Inverse scale space methods for blind deconvolution. UCLA CAM Report 06-36 (2006) Google Scholar
- 15.Rudin, L., Osher, S.J.: Total variation based image restoration with free local constraints. In Proc. of the IEEE ICIP-94, vol. 1, pp. 31–35, Austin, TX (1994) Google Scholar