# LSQ Image Intensity Matching¶

A module that provides main API for optimal (LSQ) “matching” of weighted N-dimensional image intensity data using (multivariate) polynomials.

Author: Mihai Cara (contact: help@stsci.edu) LICENSE
jwst.wiimatch.match.match_lsq(images, masks=None, sigmas=None, degree=0, center=None, image2world=None, center_cs='image', ext_return=False)[source]

Compute coefficients of (multivariate) polynomials that once subtracted from input images would provide image intensity matching in the least squares sense.

images : list of numpy.ndarray
A list of 1D, 2D, etc. numpy.ndarray data array whose “intensities” must be “matched”. All arrays must have identical shapes.
masks : list of numpy.ndarray, None
A list of numpy.ndarray arrays of same length as images. Non-zero mask elements indicate valid data in the corresponding images array. Mask arrays must have identical shape to that of the arrays in input images. Default value of None indicates that all pixels in input images are valid.
sigmas : list of numpy.ndarray, None
A list of numpy.ndarray data array of same length as images representing the uncertainties of the data in the corresponding array in images. Uncertainty arrays must have identical shape to that of the arrays in input images. The default value of None indicates that all pixels will be assigned equal weights.
degree : iterable, int
A list of polynomial degrees for each dimension of data arrays in images. The length of the input list must match the dimensionality of the input images. When a single integer number is provided, it is assumed that the polynomial degree in each dimension is equal to that integer.
center : iterable, None, optional
An iterable of length equal to the number of dimensions in image_shape that indicates the center of the coordinate system in image coordinates when center_cs is 'image' otherwise center is assumed to be in world coordinates (when center_cs is 'world'). When center is None then center is set to the middle of the “image” as center[i]=image_shape[i]//2. If image2world is not None and center_cs is 'image', then supplied center will be converted to world coordinates.
image2world : function, None, optional
Image-to-world coordinates transformation function. This function must be of the form f(x,y,z,...) and accept a number of arguments numpy.ndarray arguments equal to the dimensionality of images.
center_cs : {‘image’, ‘world’}, optional
Indicates whether center is in image coordinates or in world coordinates. This parameter is ignored when center is set to None: it is assumed to be False. center_cs cannot be 'world' when image2world is None unless center is None.
ext_return : bool, optional
Indicates whether this function should return additional values besides optimal polynomial coefficients (see bkg_poly_coeff return value below) that match image intensities in the LSQ sense. See Returns section for more details.
bkg_poly_coeff : numpy.ndarray

When nimages is None, this function returns a 1D numpy.ndarray that holds the solution (polynomial coefficients) to the system.

When nimages is not None, this function returns a 2D numpy.ndarray that holds the solution (polynomial coefficients) to the system. The solution is grouped by image.

a : numpy.ndarray
A 2D numpy.ndarray that holds the coefficients of the linear system of equations. This value is returned only when ext_return is True.
b : numpy.ndarray
A 1D numpy.ndarray that holds the free terms of the linear system of equations. This value is returned only when ext_return is True.
coord_arrays : list
A list of numpy.ndarray coordinate arrays each of image_shape shape. This value is returned only when ext_return is True.
eff_center : tuple
A tuple of coordinates of the effective center as used in generating coordinate arrays. This value is returned only when ext_return is True.
coord_system : {‘image’, ‘world’}
Coordinate system of the coordinate arrays and returned center value. This value is returned only when ext_return is True.

match_lsq() builds a system of linear equations

a \cdot c = b

whose solution c is a set of coefficients of (multivariate) polynomials that represent the “background” in each input image (these are polynomials that are “corrections” to intensities of input images) such that the following sum is minimized:

L = \sum^N_{n,m=1,n \neq m} \sum_k\frac{\left[I_n(k) - I_m(k) - P_n(k) + P_m(k)\right]^2}{\sigma^2_n(k) + \sigma^2_m(k)}.

In the above equation, index k=(k_1,k_2,...) labels a position in input image’s pixel grid [NOTE: all input images share a common pixel grid].

“Background” polynomials P_n(k) are defined through the corresponding coefficients as:

P_n(k_1,k_2,...) = \sum_{d_1=0,d_2=0,...}^{D_1,D_2,...} c_{d_1,d_2,...}^n \cdot k_1^{d_1} \cdot k_2^{d_2} \cdot \ldots .

Coefficients c_{d_1,d_2,...}^n are arranged in the vector c in the following order:

(c_{0,0,\ldots}^1,c_{1,0,\ldots}^1,\ldots,c_{0,0,\ldots}^2,c_{1,0,\ldots}^2,\ldots).

match_lsq() returns coefficients of the polynomials that minimize L.

>>> import wiimatch
>>> import numpy as np
>>> im1 = np.zeros((5, 5, 4), dtype=np.float)
>>> cbg = 1.32 * np.ones_like(im1)
>>> ind = np.indices(im1.shape, dtype=np.float)
>>> im3 = cbg + 0.15 * ind[0] + 0.62 * ind[1] + 0.74 * ind[2]