Regularization for super-resolution image reconstruction

Authors:
Vivek Bannore
Affiliations:
School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2006

Citing 3
Cited 1

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The use of the L-curve in the regularization of discrete ill-posed problems

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Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion

Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion

Search for a computationally efficient image super-resolution algorithm

KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I

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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.