Computing a higher resolution image from multiple lower resolution images using model-base, robust bayesian estimation
First Claim
Patent Images
1. A method comprising:
- acquiring an image sequence;
computing a likelihood gradient and a prior gradient; and
using the likelihood gradient and the prior gradient to update a high resolution image.
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Abstract
A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. The image reconstruction methodology may be used to enhance the image quality of images or video captured using a low resolution image capture device. Other embodiments are also described and claimed.
97 Citations
31 Claims
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1. A method comprising:
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acquiring an image sequence;
computing a likelihood gradient and a prior gradient; and
using the likelihood gradient and the prior gradient to update a high resolution image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method comprising:
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performing a super resolution operation on low resolution video to create high resolution video; and
broadcasting the high resolution video. - View Dependent Claims (12, 13, 14, 15, 16, 17)
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18. An apparatus comprising:
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a first interface to receive low resolution video content;
an image processing component coupled to the first interface, wherein the image processing component is dedicated to running a super resolution algorithm to convert the low resolution video content into high resolution video content; and
a second interface coupled to the image processing component to output the high resolution video content. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26)
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27. A method comprising:
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acquiring a plurality of one dimensional signals, wherein each of the plurality of signals is phase shifted from another;
computing a likelihood gradient and a prior gradient using the phase shift; and
using the likelihood gradient and the prior gradient to update a high resolution one dimensional signal. - View Dependent Claims (28, 29, 30, 31)
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Specification