code: def correlation2loglikelihood(rho2, npts):
      """
      Re-normalize correlation.
  
      Convert a squared normalized correlation to a proper
      log-likelihood associated with a registration problem. The result
      is a function of both the input correlation and the number of
      points in the image overlap.
  
      See: Roche, medical image registration through statistical
      inference, 2001.
  
      Parameters
      ----------
      rho2: float
        Squared correlation measure
  
      npts: int
        Number of points involved in computing `rho2`
  
      Returns
      -------
      ll: float
        Log-likelihood re-normalized `rho2`
      """
      return -0.5 * npts * np.log(nonzero(1 - rho2))
  
imports: import numpy
  from scipy.ndimage import gaussian_filter
  from ._registration import _L1_moments
label_desc: Use image node_id 1 for calling a global function with example usage: correlation2loglikelihood(rho2, npts) and returns: unknown