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US 6,725,174 B2Granted
Edge-preserving enhancement of seismic images by nonlinear anisotropic diffusion
AssigneeShell Oil Company
InventorsErik Bouts, Gijsbert Christiaan Fehmers, Stiliyan Nikolov Kalitzin
Life of this patent
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Abstract
A method for preserving edges in an image data set utilizing a modified anisotropic diffusion technique which can be applied multiple times to the image data to improve edge definition. The modified anisotropic diffusion technique may be applied to a filtered data set as well.
Claims7
A method of processing an image the steps comprising:
- (a) obtaining an n-dimensional initial image data set, wherein each element u(m=0) of the data set is the initial image intensity of a point of the image, and wherein n=2 or n=3;
- (b) calculating for each point the partial derivatives of u(m) in n directions to obtain n derivative data sets of the partial derivatives uxi;
- (c) calculating for each point a symmetric n×n structural matrix S, wherein the elements spq equal uxp·uxq;
- (d) calculating for each point an n×n diffusion matrix D, wherein the elements dij are a function of the elements spq;
- (e) calculating for each point u(m+1) from u(m) using the following equation u(m+1)=u(m)+c.div(ε.Dgrad(u(m))), wherein c is a predetermined constant, 0≦c≦1, and wherein ε is a scalar, 0≦ε≦1, wherein ε is close to zero when near an edge and close to 1 when far away from an edge;
- and
- (f) repeating steps (b) through (e) M times to obtain the processed image.
4 more elements
A method of processing an image the steps comprising:
- (a) obtaining an n-dimensional initial image data set, wherein each element u(m=0) of the data set is the initial image intensity of a point of the image, and wherein n=2 or n=3;
- (b) calculating for each point the partial derivatives of u(m) in n directions to obtain n derivative data sets of the partial derivatives Uxi;
- (c) calculating for each point a symmetric n×n structural matrix S0, wherein the elements s0pq equal up·uq;
- creating (½)n(n+1) data sets, wherein the elements of the first data set are s011 pertaining to each point, the elements of the second data set are s012 and so on;
- and filtering each of these data sets by convolution with a suitable kernel to obtain the elements spq of the n×n structural matrix S;
- and
- (d) calculating for each point ε=trace(S0S)/(trace(S0)·trace(S)) and attributing the calculated value of ε to each point to obtain an image in which faults are highlighted.
4 more elements
A method of processing an image the steps comprising:
- (a) obtaining an n-dimensional initial image data set, wherein each element u(m=0) of the data set is the initial image intensity of a point of the image, and wherein n=2 or n=3;
- (b) calculating for each point the partial derivatives of u(m) in n directions to obtain n derivative data sets of the partial derivatives uxi;
- (c) filtering for each point the partial derivatives uxi by convolution with a suitable kernel to obtain regularized partial derivatives urxi;
- calculating for each point a symmetric n×n structural precursor matrix S0, wherein the elements s0pq equal urxp·urxq;
- or creating (½)n(n+1) data sets, wherein the elements of the first data set are S011 pertaining to each point, the elements of the second data set are s012 and so on;
- and filtering each of these data sets by convolution with a suitable kernel to obtain the elements spq of the n×n structural matrix S;
- and
- (d) calculating for each point ε=trace(S0S)/(trace(S0)·trace(S)) and attributing the calculated value of ε to each point to obtain an image in which faults are highlighted.
5 more elements
Dependencies are inferred from the claim text.