Bayesian approach for sensor super-resolution
AssigneeTeledyne Licensing, LLC
InventorsMark Alan Peot, Mario Aguilar
Life of this patent
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Abstract
Bayesian super-resolution techniques fuse multiple low resolution images (possibly from multiple bands) to infer a higher resolution image. The super-resolution and fusion concepts are portable to a wide variety of sensors and environmental models. The procedure is model-based inference of super-resolved information. In this approach, both the point spread function of the sub-sampling process and the multi-frame registration parameters are optimized simultaneously in order to infer an optimal estimate of the super-resolved imagery. The procedure involves a significant number of improvements, among them, more accurate likelihood estimates and a more accurate, efficient, and stable optimization procedure.
Claims5
A computer implemented method of deriving a high resolution image from a plurality of low resolution images, comprising the steps of:
- initializing one or more alignment parameters to one or more likely values;
- determining the marginal likelihood of the low resolution images using the one or more alignment parameters, in which the marginal likelihood is a function ƒ of the alignment parameters where:
- ƒ=lg|Σ|+μTΣ−1μ,
- and μ is the mean and Σ is the variance of the posterior distribution over the high resolution image given the plurality of low resolution images;
- adjusting the alignment parameters so as to optimize the marginal likelihood determination;
- and
- determining the high resolution image using the adjusted alignment parameters.
A computer implemented method of deriving a high resolution image from a plurality of low resolution images, comprising the steps of:
- (a) sampling multiple portions of the low resolution images;
- (b) generating alignment parameters for the low resolution images using the sampled portions of the low resolution images comprising the steps of:
- (1) initializing one or more alignment parameters to one or more likely values;
- (2) determining the marginal likelihood of the low resolution images using the one or more alignment parameters, in which the marginal likelihood is a function ƒ of the alignment parameters, where:
- ƒ=lg|Σ|+μTΣ−1μ,
- and μ is the mean and Σ is the variance of the posterior distribution over the high resolution image given the plurality of low resolution images;
- and
- (3) adjusting the alignment parameters so as to optimize the marginal likelihood determination;
- and
- (c) deriving the high resolution image from the adjusted alignment parameters and the low resolution images.
Dependencies are inferred from the claim text.