Automated image registration with varied amounts of a priori information using a minimum entropy method

1. A method comprising:

providing a reference image and a source image to a processor;

wherein the processor uses a wavelet transformation to produce a transformed reference image and a transformed source image; uses the transformed reference image and the transformed source image to estimate affine transform parameters; uses the reference image, the source image, and the affine transform estimates to maximize normalized mutual information between the reference image and the source image; uses the normalized mutual information to perform sub-pixel geo-spatial registration of the reference image and the source image to produce an output image; and applies minimum and maximum affine parameter ranges as lower and upper bounds to solve non-linear equations using a modified Conjugate Gradient Descent algorithm with variable time steps to find the global maximum values of the affine transform parameters.

2. A method comprising:

providing a reference image and a source image to a processor;

wherein the processor uses a wavelet transformation to produce a transformed reference image and a transformed source image; uses the transformed reference image and the transformed source image to estimate affine transform parameters; uses the reference image, the source image, and the affine transform estimates to maximize normalized mutual information between the reference image and the source image; uses the normalized mutual information to perform sub-pixel geo-spatial registration of the reference image and the source image to produce an output image; and uses

a sequential combination of a fast discrete Wavelet Transform for radix-two down-sampling of an image pyramid, a mixed radix Fast Fourier-Mellin Transform in a log-polar coordinate space, a phase-only cross correlation, and a 3-pass shear transformation to automatically compute initial lower and upper bounds for the affine parameters to produce the transformed reference image and the transformed source image.

3. The method of claim 1 , wherein the affine parameters include:

translation, scale, rotation, and shear in x-direction and y-direction as starting values for a non-linear optimization process.

4. The method of claim 1 ,

wherein the processor applies Taguchi Techniques based on a larger-the-better of signal-to-noise ratio criterion to adjust the affine parameters around a local maxima condition and to perform geo-spatial registration to the reference image within a sub-pixel accuracy.

5. The method of claim 1 , wherein the reference image and a source image are acquired by different sensors, at different times, or from different viewpoints.

6. A method comprising:

providing a reference image and a source image to a processor;

wherein the processor uses a wavelet transformation to produce a transformed reference image and a transformed source image; uses the transformed reference image and the transformed source image to estimate affine transform parameters; uses the reference image, the source image, and the affine transform estimates to maximize normalized mutual information between the reference image and the source image; uses the normalized mutual information to perform sub-pixel geo-spatial registration of the reference image and the source image to produce an output image; and uses two filters to separate frequency content of the reference image and source image in frequency bands of equal width; and

wherein outputs of the filters, each contain half the frequency content, but an equal amount of samples as the reference image and source image.

7. The method of claim 1 , wherein the output image is down-sampled by a factor of two.

8. An apparatus comprising:

a sensor for producing a reference image and a source image; and

a processor for using a wavelet transformation to produce a transformed reference image and a transformed source image, using the transformed reference image and the transformed source image to estimate affine transform parameters, using the reference image, the source image, and the affine transform estimates to maximize normalized mutual information between the reference image and the source image, and using the normalized mutual information to perform sub-pixel geo-spatial registration of the reference image and the source image, wherein minimum and maximum affine parameter ranges are applied as lower and upper bounds to solve non-linear equations using a modified Conjugate Gradient Descent algorithm with variable time steps to find the global maximum values of the affine transform parameters.

9. An apparatus comprising:

a sensor for producing a reference image and a source image; and

a processor for using a wavelet transformation to produce a transformed reference image and a transformed source image, using the transformed reference image and the transformed source image to estimate affine transform parameters, using the reference image, the source image, and the affine transform estimates to maximize normalized mutual information between the reference image and the source image, and using the normalized mutual information to perform sub-pixel geo-spatial registration of the reference image and the source image, wherein the processor uses a sequential combination of a fast discrete Wavelet Transform for radix-two down-sampling of an image pyramid, a mixed radix Fast Fourier-Mellin Transform in a log-polar coordinate space, a phase-only cross correlation, and a 3-pass shear transformation to automatically compute initial lower and upper bounds for the affine parameters.

10. The apparatus of claim 8 , wherein the affine parameters include:

translation, scale, rotation, and shear in x-direction and y-direction as starting values for a non-linear optimization process.

11. The apparatus of claim 8 , wherein the processor further applies Taguchi Techniques based on a larger-the-better of signal-to-noise ratio criterion to adjust the affine parameters around a local maxima condition and to perform geo-spatial registration to the reference image within a sub-pixel accuracy.