Abstract
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.
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Keywords
- Inverse Problem
- Singular Value Decomposition
- Regularization Parameter
- Regularization Term
- Image Restoration
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Bannore, V. (2006). Regularization for Super-Resolution Image Reconstruction. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_5
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DOI: https://doi.org/10.1007/11893004_5
Publisher Name: Springer, Berlin, Heidelberg
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