SELECTIVE DIFFUSION OF FILTERED EDGES IN IMAGES
First Claim
1. A method of analyzing an image, the method comprising:
- receiving at least one unfiltered image and at least one corresponding filtered image;
selecting a first image element of the filtered image;
selecting a first blurring operator;
applying the first blurring operator to a neighborhood surrounding the first image element to determined a blurred image element, wherein the blurring operator is adapted to reduce a magnitude of a high spatial frequency component of an image spectrum relative to a magnitude of a low spatial frequency component of the image spectrum;
evaluating an error metric to compare the blurred image element and the first image element with a corresponding image element of the unfiltered image;
generating an output image including at least a first output image element; and
in response to the error metric satisfying a first criteria, setting the first output image element to equal the blurred image element.
1 Assignment
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Accused Products
Abstract
An edge-preserving diffusion filter maintains the sharp edges in images while smoothing out image noise. An edge-preserving diffusion filter applies an edge-preserving smoothing filter to an image to form a filtered image. The modified image is then blurred by a blurring filter to form a blurred image. The modified image and the blurred image are blended together to form an output image based on an error metric associated with each pixel. The edge-preserving diffusion filter may be utilized to perform a multilevel decomposition of the image. The edge-preserving diffusion filter may be applied to an unfiltered image to produce a base image. The difference between the unfiltered image and the base image defines a detail image. The detail image may be used as the input for recursively generating additional levels of detail. The multilevel decomposition may utilize filter kernels associated with different contrast levels for each iteration.
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Citations
30 Claims
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1. A method of analyzing an image, the method comprising:
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receiving at least one unfiltered image and at least one corresponding filtered image; selecting a first image element of the filtered image; selecting a first blurring operator; applying the first blurring operator to a neighborhood surrounding the first image element to determined a blurred image element, wherein the blurring operator is adapted to reduce a magnitude of a high spatial frequency component of an image spectrum relative to a magnitude of a low spatial frequency component of the image spectrum; evaluating an error metric to compare the blurred image element and the first image element with a corresponding image element of the unfiltered image; generating an output image including at least a first output image element; and in response to the error metric satisfying a first criteria, setting the first output image element to equal the blurred image element. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method of analyzing an image, the method comprising:
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receiving at least one unfiltered image; applying a first edge-preserving diffusion filter to the unfiltered image to form a first base image; generating a first detail image based on differences between the unfiltered image and the first base image; and outputting the first base image and at least one detail image. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21)
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22. A method of analyzing an image, the method comprising:
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receiving a first image; applying a first filter to the first image to create a filtered image; applying a first blurring operator to the filtered image to create a blurred image; comparing each of the filtered image and the blurred image with the first image to identify at least a first portion of the blurred image that is closer to the first image than the filtered image and at least a second portion of the filtered image that is closer to the first image than the blurred image; and creating an output image including the first portion of the blurred image and the second portion of the filtered image. - View Dependent Claims (23, 24, 25, 26, 27, 28, 29, 30)
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Specification