Method, an apparatus and a computer-readable medium for processing a night vision image dataset
First Claim
1. A method for processing a sequence of night vision image datasets, wherein said sequence comprises at least two image datasets each having at least two pixels, wherein each pixel has an intensity value, said method comprising:
- calculating, based on estimated intensity derivatives in each pixel, a structure tensor for each pixel in an image dataset comprised in said sequence of image datasets, and performing an eigen-decomposition of said structure tensor,calculating values in a summation kernel based on said structure tensor for each pixel in said image dataset,calculating a weighted intensity value for each pixel in said image dataset, using as weights the values in said summation kernel,storing said weighted intensity value for each pixel in said image dataset as a processed intensity value for each corresponding pixel in a processed output image dataset,the method furthermore comprising;
rotating a local coordinate system in which the summation kernel is described resulting in that the coordinate axes of said local coordinate system coincide with the directions of the eigenvectors of said structure tensor, where said eigenvectors are described in the global coordinate system of the image dataset, andscaling the coordinate axes of the local coordinate system in which the summation kernel is described by an amount related to the eigenvalues of the structure tensor via a width function w(λ
i)=σ
i, and wherein said eigenvalues depend on the amount of intensity variation in the direction of their corresponding eigenvectors,and in addition the width function being a decreasing function depending on the noise level in each pixel such that w(0)=σ
max and lima→
∞
w(a)=σ
min.
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Accused Products
Abstract
Method for processing a sequence of at least two image datasets each having at least two pixels, each pixel having an intensity value by calculating a structure tensor for each pixel in an image dataset included in the sequence of image datasets; calculating values in a summation kernel based on the structure tensor for each pixel in the image dataset; calculating a weighted intensity value for each pixel in the first image dataset, using as weights the values in the summation kernel; storing the weighted intensity value for each pixel in the image dataset as a processed intensity value for each corresponding pixel in a processed output image dataset; rotating a local coordinate system in which the summation kernel is described so that the coordinate axes of said local coordinate system coincide with the directions of the eigenvectors of the structure tensor.
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Citations
19 Claims
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1. A method for processing a sequence of night vision image datasets, wherein said sequence comprises at least two image datasets each having at least two pixels, wherein each pixel has an intensity value, said method comprising:
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calculating, based on estimated intensity derivatives in each pixel, a structure tensor for each pixel in an image dataset comprised in said sequence of image datasets, and performing an eigen-decomposition of said structure tensor, calculating values in a summation kernel based on said structure tensor for each pixel in said image dataset, calculating a weighted intensity value for each pixel in said image dataset, using as weights the values in said summation kernel, storing said weighted intensity value for each pixel in said image dataset as a processed intensity value for each corresponding pixel in a processed output image dataset, the method furthermore comprising; rotating a local coordinate system in which the summation kernel is described resulting in that the coordinate axes of said local coordinate system coincide with the directions of the eigenvectors of said structure tensor, where said eigenvectors are described in the global coordinate system of the image dataset, and scaling the coordinate axes of the local coordinate system in which the summation kernel is described by an amount related to the eigenvalues of the structure tensor via a width function w(λ
i)=σ
i, and wherein said eigenvalues depend on the amount of intensity variation in the direction of their corresponding eigenvectors,and in addition the width function being a decreasing function depending on the noise level in each pixel such that w(0)=σ
max and lima→
∞
w(a)=σ
min. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. An apparatus for processing a sequence of night vision image datasets, said sequence comprising at least two image datasets each having at least two pixels, wherein each pixel has an intensity value, said apparatus comprising:
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a first calculation unit for calculating, based on estimated intensity derivatives in each pixel, a structure tensor for each pixel in an image dataset comprised in said sequence of image datasets, and performing an eigen-decomposition of said structure tensor, a second calculation unit for calculating values in a summation kernel based on said structure tensor for each pixel in said image dataset, a third calculation unit for calculating a weighted intensity value for each pixel in said image dataset, using as weights the values in said summation kernel, a storage unit for storing said weighted intensity value for each pixel in said image dataset as a processed intensity value for each corresponding pixel in a processed output image dataset, wherein the apparatus further comprises; a calculation unit for rotating a local coordinate system in which the summation kernel is described resulting in that the coordinate axes of said local coordinate system coincide with the directions of the eigenvectors of said structure tensor, where said eigenvectors are described in the global coordinate system of the image dataset, and a scaling unit for scaling the coordinate axes of the local coordinate system in which the summation kernel is described by an amount related to the eigenvalues of the structure tensor via a width function w(λ
i)=σ
i, and wherein said eigenvalues depend on the amount of intensity variation in the direction of their corresponding eigenvectors,and in addition the width function being a decreasing function depending on the noise level in each pixel such that w(0)=σ
max and lima→
∞
w(a)=σ
min. - View Dependent Claims (15, 16)
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17. A computer-readable medium having embodied thereon a computer program for processing by a processor of a sequence of image datasets, wherein said sequence comprises at least two image datasets, each image dataset having at least two pixels, wherein each pixel has an intensity value, said computer program comprising
a first calculation code segment for calculating, based on estimated intensity derivatives in each pixel, a structure tensor for each pixel in an image dataset comprised in said sequence of image datasets, and for performing an eigen-decomposition of said structure tensor, a second calculation code segment for calculating values in a summation kernel based on said structure tensor for each pixel in said image dataset, a third calculation code segment for calculating a weighted intensity value for each pixel in said image dataset, using as weights the values in said summation kernel, a storage code segment for storing said weighted intensity value for each pixel in said image dataset as a processed intensity value for each corresponding pixel in a processed output image dataset, a further calculation code segment for rotating a local coordinate system in which the summation kernel is described resulting in that the coordinate axes of said local coordinate system coincide with the directions of the eigenvectors of said structure tensor, where said eigenvectors are described in the global coordinate system of the image dataset, and a scaling code segment for scaling the coordinate axes of the local coordinate system in which the summation kernel is described by an amount related to the eigenvalues of the structure tensor via a width function w(λ -
i)=σ
i, and wherein said eigenvalues depend on the amount of intensity variation in the direction of their corresponding eigenvectors,and in addition the width function being a decreasing function depending on the noise level in each pixel such that w(0)=σ
max and lima→
∞
w(a)=σ
min. - View Dependent Claims (18, 19)
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i)=σ
Specification