Bayesian approach for sensor super-resolution

• US 20070130095A1
• Filed: 09/30/2005
• Published: 06/07/2007
• Est. Priority Date: 09/30/2005
• Status: Active Grant

• Current Assignee: Teledyne Scientific & Imaging, LLC (Teledyne Technologies Incorporated)
• Original Assignee: Teledyne Licensing, LLC
• Inventors: Aguilar, Mario, Peot, Mark
• Granted Patent: US 7,523,078 B2
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06T 3/4053 based on super-resolution, i.e. the output image resolution being higher than the sensor resolution

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.

Claims

1. A method of deriving a high resolution image from a plurality of low resolution images, comprising the steps of:

initializing one of 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 f of the alignment and deblurring parameters where;

f=lg|Σ

|+μ

^TΣ

<sup>−

1</sup>μ

,

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.

2. A method of deriving a high resolution image from a plurality of low resolution images, comprising the steps of:

initializing one of 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 f of the alignment parameters where;

f=−

lg|P|+μ

^TPμ

,

and P is the precision of the posterior distribution and μ

is the mean of the distribution modeled for the high resolution image;

adjusting the alignment parameters so as to optimize the marginal likelihood determination; and

determining the high resolution image using the adjusted alignment parameters.

3. A method of deriving a high resolution image from a plurality of low resolution images, comprising the steps of:

sampling multiple portions of the low resolution images;

generating registration parameters for the low resolution images using the selected one or more portions of the low resolution images; and

deriving the high resolution image from the registration parameters for the selected one or more portions of the low resolution images.