image_registration Package¶

`image_registration` Package¶

image_registration Package¶

This is an Astropy affiliated package.

Functions¶

`chi2_shift`(im1, im2[, err, upsample_factor, ...])	Find the offsets between image 1 and image 2 using the DFT upsampling method (http://www.mathworks.com/matlabcentral/fileexchange/18401-efficient-subpixel-image-registration-by-cross-correlation/content/html/efficient_subpixel_registration.html) combined with \(\chi^2\) to measure the errors on the fit
`chi2_shift_iterzoom`(im1, im2[, err, ...])	Find the offsets between image 1 and image 2 using an iterative DFT upsampling method combined with \(\chi^2\) to measure the errors on the fit
`chi2n_map`(im1, im2[, err, boundary, ...])	Parameters:
`cross_correlation_shifts`(image1, image2[, ...])	Use cross-correlation and a 2nd order taylor expansion to measure the offset between two images
`cross_correlation_shifts_FITS`(fitsfile1, ...)	Determine the shift between two FITS images using the cross-correlation technique.
`register_images`(im1, im2[, usfac, ...])	Sub-pixel image registration (see dftregistration for lots of details)
`test`(**kwargs)	Run the tests for the package.

`register_images` Module¶

image_registration.register_images Module¶

Functions¶

register_images(im1, im2[, usfac, ...])

Sub-pixel image registration (see dftregistration for lots of details)

image_registration.register_images.register_images(im1, im2, usfac=1, return_registered=False, return_error=False, zeromean=True, DEBUG=False, maxoff=None, nthreads=1, use_numpy_fft=False)[source]

Sub-pixel image registration (see dftregistration for lots of details)

Parameters:

im1np.ndarray
im2np.ndarray: The images to register.
usfacint: upsampling factor; governs accuracy of fit (1/usfac is best accuracy)
return_registeredbool: Return the registered image as the last parameter
return_errorbool: Does nothing at the moment, but in principle should return the “fit error” (it does nothing because I don’t know how to compute the “fit error”)
zeromeanbool: Subtract the mean from the images before cross-correlating? If no, you may get a 0,0 offset because the DC levels are strongly correlated.
maxoffint: Maximum allowed offset to measure (setting this helps avoid spurious peaks)
DEBUGbool: Test code used during development. Should DEFINITELY be removed.

Returns:

dx,dyfloat,float: REVERSE of dftregistration order (also, signs flipped) for consistency with other routines. Measures the amount im2 is offset from im1 (i.e., shift im2 by these #’s to match im1)

`chi2_shifts` Module¶

image_registration.chi2_shifts Module¶

Chi^2 shifts¶

Various tools for calculating shifts based on the chi^2 method

Functions¶

chi2_shift(im1, im2[, err, upsample_factor, ...])

Find the offsets between image 1 and image 2 using the DFT upsampling method (http://www.mathworks.com/matlabcentral/fileexchange/18401-efficient-subpixel-image-registration-by-cross-correlation/content/html/efficient_subpixel_registration.html) combined with \(\chi^2\) to measure the errors on the fit

chi2_shift_iterzoom(im1, im2[, err, ...])

Find the offsets between image 1 and image 2 using an iterative DFT upsampling method combined with \(\chi^2\) to measure the errors on the fit

chi2n_map(im1, im2[, err, boundary, ...])

Parameters:

`cross_correlation_shifts` Module¶

image_registration.cross_correlation_shifts Module¶

Functions¶

`cross_correlation_shifts`(image1, image2[, ...])	Use cross-correlation and a 2nd order taylor expansion to measure the offset between two images
`cross_correlation_shifts_FITS`(fitsfile1, ...)	Determine the shift between two FITS images using the cross-correlation technique.

image_registration.cross_correlation_shifts.cross_correlation_shifts(image1, image2, errim1=None, errim2=None, maxoff=None, verbose=False, gaussfit=False, return_error=False, zeromean=True, **kwargs)[source]

Use cross-correlation and a 2nd order taylor expansion to measure the offset between two images

Given two images, calculate the amount image2 is offset from image1 to sub-pixel accuracy using 2nd order taylor expansion.

Parameters:

image1: np.ndarray: The reference image
image2: np.ndarray: The offset image. Must have the same shape as image1
errim1: np.ndarray [optional]: The pixel-by-pixel error on the reference image
errim2: np.ndarray [optional]: The pixel-by-pixel error on the offset image.
maxoff: int: Maximum allowed offset (in pixels). Useful for low s/n images that you know are reasonably well-aligned, but might find incorrect offsets due to edge noise
zeromeanbool: Subtract the mean from each image before performing cross-correlation?
verbose: bool: Print out extra messages?
gaussfitbool: Use a Gaussian fitter to fit the peak of the cross-correlation?
return_error: bool: Return an estimate of the error on the shifts. WARNING: I still don’t understand how to make these agree with simulations. The analytic estimate comes from http://adsabs.harvard.edu/abs/2003MNRAS.342.1291Z At high signal-to-noise, the analytic version overestimates the error by a factor of about 1.8, while the gaussian version overestimates error by about 1.15. At low s/n, they both UNDERestimate the error. The transition zone occurs at a total S/N ~ 1000 (i.e., the total signal in the map divided by the standard deviation of the map - it depends on how many pixels have signal)
**kwargs are passed to correlate2d, which in turn passes them to convolve.
The available options include image padding for speed and ignoring NaNs.

References

From http://solarmuri.ssl.berkeley.edu/~welsch/public/software/cross_cor_taylor.pro

Examples

>>> import numpy as np
>>> im1 = np.zeros([10,10])
>>> im2 = np.zeros([10,10])
>>> im1[4,3] = 1
>>> im2[5,5] = 1
>>> import image_registration
>>> yoff,xoff = image_registration.cross_correlation_shifts(im1,im2)
>>> im1_aligned_to_im2 = np.roll(np.roll(im1,int(yoff),1),int(xoff),0)
>>> assert (im1_aligned_to_im2-im2).sum() == 0

image_registration Package¶