ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
A two-step neural-network based algorithm for fast image super-resolution
Image and Vision Computing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A soft MAP framework for blind super-resolution image reconstruction
Image and Vision Computing
High Resolution and High Dynamic Range Image Reconstruction from Differently Exposed Images
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A 3-D assisted generative model for facial texture super- resolution
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Adaptive multiple-frame image super-resolution based on U-curve
IEEE Transactions on Image Processing
Adaptive MAP high-resolution image reconstruction algorithm using local statistics
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
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In this paper a solution is provided to the problem of obtaining a high resolution image from several low resolution images that have been subsampled and displaced by different amounts of sub-pixel shifts. In its most general form, this problem can be broken up into three sub-problems: registration, restoration, and interpolation. Previous work has either solved all three sub-problems independently, or more recently, solved either the first two steps (registration and restoration) or the last two steps together. However, none of the existing methods solve all three sub-problems simultaneously. This paper poses the low resolution to high resolution problem as a maximum likelihood (ML) problem which is solved by the expectation-maximization (EM) algorithm. By exploiting the structure of the matrices involved, the problem ran be solved in the discrete frequency domain. The ML problem is then the estimation of the sub-pixel shifts, the noise variances of each image, the power spectra of the high resolution image, and the high resolution image itself. Experimental results are shown which demonstrate the effectiveness of this approach.