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US 9,001,231 B2Granted
Image acquisition using oversampled one-bit poisson statistics
AssigneeRambus Inc.
InventorsYue Lu, Feng Yang, Martin Vetterli
Life of this patent
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Abstract
In an image sensor pixels in an array of binary pixels are sampled with a first oversampling factor during a first frame interval, and then sampled with a second oversampling factor during a second frame interval, the second oversampling factor exceeding the first oversampling factor.
Claims14
An image acquisition method using an image sensor comprising an array of one-bit pixels,
- wherein the size of the pixels is below the fundamental diffraction limit of visible light, so that the image sensor is oversampling the optical resolution of visible light,
- wherein each pixel in said image sensor delivers a one-bit quantized measurement of the local light intensity received at that pixel during an acquisition period,
- wherein an image is reconstructed from the measurements of said pixels using a maximum likelihood estimation algorithm in order to retrieve coefficients (cn) of a sum of interpolation kernels corresponding to said image,
- wherein the numerical solution of said maximum likelihood estimator is computed using a standard gradient ascent method.
1 more element
An image acquisition method using an image sensor comprising an array of one-bit pixels,
- wherein the size of the pixels is below the fundamental diffraction limit of visible light, so that the image sensor is oversampling the optical resolution of visible light,
- wherein each pixel in said image sensor delivers a one-hit quantized measurement of the local light intensity received at that pixel during an acquisition period,
- wherein an image is reconstructed from the measurements of said pixels using a maximum likelihood estimation algorithm in order to retrieve coefficients (cn) of a sum of interpolation kernels corresponding to said image,
- wherein the numerical solution of said maximum likelihood estimator is computed using an iterative method.
1 more element
An image acquisition apparatus comprising:
- an image sensor comprising an array of one-bit pixels, wherein the size of the pixels is below the fundamental diffraction limit of visible light, so that the image sensor is oversampling the optical resolution of visible light, wherein each pixel in said image sensor is arranged for delivering a one-bit quantized measurement of the local light intensity during an acquisition period,
- an image processing circuit for reconstructing an image from said measurements using a maximum likelihood estimation algorithm in order to retrieve coefficients (cn) of a sum of interpolation kernels corresponding to said image, wherein the numerical solution of said maximum likelihood estimator is computed iteratively by the image processing circuit.
A method of operating an image sensor having an array of binary pixels, the method comprising:
- during a first frame interval, sampling pixels in the array of binary pixels with a first oversampling factor;
- during a second frame interval substantially equivalent in exposure value to the first frame interval, sampling pixels in the array of binary pixels with a second oversampling factor, the second oversampling factor exceeding the first oversampling factor;
- and
- creating a multilevel-pixel downsampled image from pixel samples gathered during the second frame interval, wherein:
- the ratio of pixel samples gathered during the second frame interval multilevel pixels is substantially equal to the second oversampling factor,
- multilevel-pixels in the downsampled image have a value corresponding to a maximum-likelihood estimate of the light exposure for the pixel samples corresponding to that multilevel pixel, and
- the maximum likelihood estimate for a multilevel sample is
- c
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- if
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- otherwise
- ,
- where K1 is the number of pixel samples, corresponding to the multilevel sample, that detected a threshold number of photon strikes, p0(−1)(x) is the inverse function of p0(x), p0(x) expresses the likelihood of K1 given a light intensity c, S is a maximum result value, and K is the second oversampling factor.
57 more elements
Dependencies are inferred from the claim text.